Systems-level analysis of 32 TCGA cancers reveals disease-dependent tRNA fragmentation patterns and very selective associations with messenger RNAs and repeat elements

ABSTRACT

Methods of treating a disease by leveraging positive and negative correlations between tRNA-derived fragments (tRF) and messenger RNA (mRNA) wherein said correlations can be used to establish a level of granularity that is specific to a disease of interest wherein said disease-specific positive and negative correlations can allow a level of therapeutic intervention that will be unprecedented because it will have been informed by three dimensions: at least one mRNA of interest; at least one tRF that are positively/negatively correlated with it; and, the identity of the disease in which one wishes to modulate the abundance of the at least one mRNA of interest.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a National Phase of International Application No.PCT/US2018/30525, filed May 1, 2018, which claims priority to U.S.Provisional Patent Application No. 62/492,662, filed May, 2017, whichare incorporated herein by reference in their entirety.

SEQUENCE LISTING

The instant application contains a Sequence Listing which has been filedelectronically in ASCII format and is hereby incorporated by referencein its entirety. Said ASCII copy, created on May 1, 2018, is named6107_178PCT_403809_SEQ_ID.txt and is 54,138 bytes in size.

FIELD OF INVENTION

This application is generally related to the use of tRNA fragments asnovel therapeutics that can target specific messenger RNAs in cancersand other diseases.

BACKGROUND OF INVENTION

Activity in recent years has been drawing increasing attention to a newgroup of molecules that appear to be produced at the same time astransfer RNAs (tRNAs). These molecules are referred to as “tRNA-derivedfragments” or tRFs and are believed to arise from both the precursor andthe mature tRNAs (1-3). For those tRFs that overlap the span of themature tRNA, four structural categories were reported originally:5′-tRFs, 3′-tRFs, 5′-halves (5′-tRHs), and 3′-halves (3′-tRHs). In arecent analysis of hundreds of human tissues, we reported a fifthstructural category, the “internal tRFs” or i-tRFs that comprisesnumerous members expressed in high abundance (4). In the same analysis,we also demonstrated that the identity and abundance of tRFs depends onpreviously unrecognized variables such as a person's sex, populationorigin, and race as well as on tissue, tissue state, and disease subtype(4). Despite these dependencies, samples from the same tissue obtainedfrom individuals with the same sex, race and disease subtype were foundto express the same tRFs and with the same relative abundances, whichindicates that these molecules are constitutive (4). More recent workshowed that tRNA “halves” can be produced under stress conditions (5,6)as well as constitutively (7-9) and to exist in variants that are notvisible by standard RNA-seq (7).

In terms of function, tRFs have been shown to associate with Argonaute(10) in a cell-type specific manner (4). This indicates that at least asubset of tRFs enter the RNA interference (RNAi) pathway. In addition,tRFs have been shown to be produced differentially in response toinfections (11,12), in cancer tissues compared to normal (4,13,14), tobe affected by diet (15), by trauma (16), to be involved intrans-generational inheritance (17), and to regulate translation (18).

In summary, there is very strong evidence that tRFs: 1) represent anovel category of regulatory molecules in their own right; 2) areimportant in homeostasis and in disease; and, 3) warrant in-depthstudies (19,20). In this presentation, we extend our earlier work (4) tothe entirety of the TCGA collection. Specifically, we processed 11,198cancer samples representing 32 cancer types with an emphasis onidentifying intra- and inter-cancer features involving tRFs. The 32cancer types included: ACC (Adrenocortical carcinoma), BLCA (BladderUrothelial Carcinoma), BRCA (Breast invasive carcinoma), CESC (Cervicalsquamous cell carcinoma and endocervical adenocarcinoma), CHOL(Cholangiocarcinoma), COAD (Colon adenocarcinoma), DLBC (LymphoidNeoplasm Diffuse Large B-cell Lymphoma), ESCA (Esophageal carcinoma),HNSC (Head and Neck squamous cell carcinoma), KICH (Kidney Chromophobe),KIRC (Kidney renal clear cell carcinoma), KIRP (Kidney renal papillarycell carcinoma), LAML (Acute Myeloid Leukemia), LGG (Brain Lower GradeGlioma), LIHC (Liver hepatocellular carcinoma), LUAD (Lungadenocarcinoma), LUSC (Lung squamous cell carcinoma), MESO(Mesothelioma), OV (Ovarian serous cystadenocarcinoma), PAAD (Pancreaticadenocarcinoma), PCPG (Pheochromocytoma and Paraganglioma), PRAD(Prostate adenocarcinoma), READ (Rectum adenocarcinoma), SARC (Sarcoma),SKCM (Skin Cutaneous Melanoma), STAD (Stomach adenocarcinoma), TGCT(Testicular Germ Cell Tumors), THCA (Thyroid carcinoma), THYM (Thymoma),UCEC (Uterine Corpus Endometrial Carcinoma), UCS (UterineCarcinosarcoma), and UVM (Uveal Melanoma). Lastly, where relevant, weuse the NIH/TCGA designations to refer to race groups (see Methods).

SUMMARY OF INVENTION

We have identified many tRNA fragments (tRFs) that form positively- andnegatively-correlated pairs with messenger RNAs (mRNAs). When we comparethe tRFs that are expressed in any two of the 32 cancers, we see thatthey largely agree. I.e., for the purposes of this analysis, largely thesame tRFs are expressed in each of the 32 cancers. When we compare themiRNAs that are expressed in any two of the 32 cancers, we see that theylargely agree. I.e., for the purposes of this analysis, largely the samemiRNAs are expressed in each of the 32 cancers. When we compare themRNAs that are expressed in any two of the 32 cancers, we see that theylargely agree. I.e., for the purposes of this analysis, largely the samemRNAs are expressed in each of the 32 cancers.

However, things change when we focus only on the subset of tRFs, miRNAs,or mRNAs that participate in tRF:mRNA or miRNA:mRNA pairs and are eithernegatively-correlated or positively-correlated. In particular, when wefocus on tRFs that participate in either positively-correlated ornegatively-correlated tRF:mRNA pairs, and compare them across cancers,we see that they are largely cancer-specific. When we focus on miRNAsthat participate in either positively-correlated ornegatively-correlated miRNA:mRNA pairs, and compare them across cancers,we see that they are largely cancer-specific. When we focus on the mRNAsthat are either positively-correlated or negatively-correlated witheither tRFs or miRNAs, and compare them across cancers, we see that theyare largely cancer-specific.

In other words, the mRNAs that are either positively-correlated ornegatively correlated with either tRFs or miRNAs in cancer X have littleoverlap with the mRNAs that are either positively-correlated ornegatively correlated with either tRFs or miRNAs in cancer Y. However,the pathways to which these mRNAs belong are conserved across cancers.This last statement captures a very important finding. Essentially, itstates that even though the tRFs in cancer X and in cancer Y aredisrupting the same pathway they do so by disrupting differentgene-members of the pathway.

FIG. 8 provides a tangible example of this observation with the help oftwo cancers: colon adenocarcinoma (COAD) and pancreatic adenocarcinoma(PAAD). The shown pathway is a signal transduction pathway known as “thePI3K-AKT pathway.” This pathway is activated by extracellular stimuli,promotes cell survival and growth, and is very often deregulated in thecancer context. In the solid-line-contour rectangle we indicate how manytRFs are produced from isodecoders of the shown nuclear andmitochondrial tRNAs and are correlated (either negatively or positively)with PI3K-AKT pathway mRNAs in COAD but not in PAAD. In thedashed-line-contour rectangle we indicate the analogous information forPAAD. Finally, in the compound-contour (triple-line) rectangle weindicate how many tRFs are produced from isodecoders of the shownnuclear and mitochondrial tRNAs and are correlated with PI3K-AKT pathwaymRNAs in both COAD and in PAAD. Note, however, that the tRFs that arecommon in COAD and PAAD may be correlated with the same or withdifferent mRNAs in these two cancers. Or, these tRFs could be correlatedwith the same mRNA in the two cancers but the correlation could have adifferent sign in each cancer, e.g. the correlation could be positive inCOAD and negative in PAAD. As can be seen, comparatively fewer tRFs thatare common to both cancer types participate in correlated pairs. Amongthe PI3K-AKT genes, the mRNAs of those genes shown with the solidcontour are correlated with tRFs only in COAD. Analogously, the mRNAs ofthe pathway's genes with the dashed contour are correlated with tRFsonly in PAAD. Finally, the mRNAs of the pathway's genes with thecompound-line contour are correlated with tRFs in COAD and in PAAD (butnot necessarily with the same tRF in the two cancers).

mRNAs from the PI3K-AKT pathway are correlated with 54 tRFs in COAD and48 tRFs in PAAD. In terms of origin, these tRFs differ radically: inCOAD, 48 of the 54 tRFs are produced by mitochondrial tRNAs; in PAAD, 32of the 45 tRFs are produced by nuclear tRNAs. The tRFs that arecorrelated with mRNAs from the PI3K-AKT pathway are largely unique toeach cancer: 44 of the 54 tRFs are specific to COAD (and, thus, absentfrom PAAD); 35 of the 45 tRFs are specific to PAAD. Here, it is alsoimportant to stress that the differences in tRFs do not arise fromendpoint variations of the same precursor molecule; instead the tRFsthat participate in correlated pairs with mRNAs originate fromcompletely distinct tRNA templates in each cancer.

Equally striking is the relatively small overlap of the affected mRNAsin each cancer. In PAAD, the tRFs are correlated with many mRNAs thatare unique to PAAD and are located “deep” (i.e. further downstream)inside the pathway (nodes with a dashed contour). On the other hand, inCOAD, the tRFs that are correlated with mRNAs that are unique to COADare characteristically at the input layer to the pathway (nodes with asolid contour).

The pathway's members that are correlated with tRFs in both COAD andPAAD (nodes with a compound-line contour) deserve special mention. As weindicated above, the fact that these genes' mRNAs are correlated withtRFs in both cancers does not necessarily mean that they are partneredwith the same tRF in both cancers, or that the sign of the correlationis the same in both cancers. Let us take the integrin alpha subunits(ITGA box/compound-line contour). In COAD, the subunits ITGA1, ITGA4,ITGA5, ITGAV, and ITGAX are correlated with many mitochondrial tRFs thatare present only in COAD (but not in PAAD). IN PAAD, the subunit ITGA7is correlated with a mitochondrial tRF that is present only in PAAD (butnot in COAD). Lastly, two mitochondrial tRFs are correlated with thesubunits ITGA1, ITGA4, and ITGAV in both COAD and PAAD.

Note that, in this example and accompanying Figure, we focused onpositively- and negatively-correlated tRF:mRNA pairs. It is reasonableto assume that at least some of the negative correlations are indicativeof direct molecular interactions between tRFs and mRNAs. As a matter offact, we and others have provided evidence that tRFs can be loaded onArgonaute (i.e. can act like miRNAs), thereby entering the RNAinterference pathway and having functional downstream effects. Anincrease in the abundance of an Argonaute-loaded tRF would result in adecrease in the abundance of its natural targets.

A tRF can also lead to a down-regulation of another transcript through amethod that is known as “decoying.” Recent publications providedexperimental evidence that a tRF can act as a molecular decoy to an RNAbinding protein (RBP) that would otherwise stabilize its target. Bydecoying the (stabilizing) RBP, the tRF would induce an apparentdecrease in the abundance of the protein's natural target(s).

Our analyses also included positively-correlated tRF:mRNA pairs. Again,it is reasonable to assume that at least some of these positivecorrelations are indicative of direct molecular interactions, presumablythrough “decoying” of a miRNA or of a destabilizing RBP. The decoyingtranscript can be non-coding or protein coding. In this discussion,without loss of generality, we focus on decoying transcripts that aretRFs. In the case of a miRNA or of a destabilizing RBP, the decoyingtranscript would sponge away the miRNA or the RBP, leading to anapparent increase in the abundance of the miRNA's or RBP's naturaltarget(s).

Both positively- and negatively-correlated tRF:mRNA pairs suggest manyand terrific opportunities for therapeutic intervention. As FIG. 8shows, these elaborate relationships highlight a level of granularitythat is specific to each cancer type and which is now known to us. Ifharnessed correctly, the cancer-specific positive and negativecorrelations that we have uncovered have the potential to permit a levelof therapeutic intervention on an mRNA that will be unprecedentedbecause it will have been informed by three dimensions:

-   -   the mRNA of interest;    -   the tRFs that are positively/negatively correlated with it; and,    -   the cancer type in which one wishes to target this mRNA.

For example, let us assume that one wishes to “target” the same genee.g. ITGAV (CD51), in multiple cancers, e.g. COAD and PAAD. ITGAV is agene whose increased levels have been linked with tumorigenicity in bothCOAD and PAAD. In the case of COAD, there are 18 different tRFs that arecorrelated with ITGAV's mRNA and could be modulated in order to affectits levels. On the other hand, our analysis shows that in the case ofPAAD there is only one such tRF (from the mitochondrial Phe tRNA) thatcan be used for this purpose.

It is important to know the cancer type as the cancer type frames thecontext that defines which tRF will interact with which mRNA, andwhether a tRF and an mRNA are positively- or negatively-correlated. Thetherapeutic intervention will aim to counterbalance the mRNAderegulation. But because the mRNAs can participate in differentpositive or negative correlations in different cancers, it means thatthe therapeutic intervention may need to have opposing goals indifferent cancer types for the same mRNA.

Preferred embodiments are directed towards a method of treating adisease in a patient comprising: selecting at least one gene that istranscribed in the ailing tissue of the said patient; selecting at leastone tRF that is correlated with the at least one gene that istranscribed in the ailing tissue of the said patient; and administeringa therapeutic composition sufficient to modify the abundance of the atleast one tRF or of the at least one gene. Ailing tissue meaning atissue that is diseased, most preferably, that disease is a cancer.

In preferred embodiments, the cancer can be one of: ACC (Adrenocorticalcarcinoma), BLCA (Bladder Urothelial Carcinoma), BRCA (Breast invasivecarcinoma), CESC (Cervical squamous cell carcinoma and endocervicaladenocarcinoma), CHOL (Cholangiocarcinoma), COAD (Colon adenocarcinoma),DLBC (Lymphoid Neoplasm Diffuse Large B-cell Lymphoma), ESCA (Esophagealcarcinoma), HNSC (Head and Neck squamous cell carcinoma), KICH (KidneyChromophobe), KIRC (Kidney renal clear cell carcinoma), KIRP (Kidneyrenal papillary cell carcinoma), LAML (Acute Myeloid Leukemia), LGG(Brain Lower Grade Glioma), LIHC (Liver hepatocellular carcinoma), LUAD(Lung adenocarcinoma), LUSC (Lung squamous cell carcinoma), MESO(Mesothelioma), OV (Ovarian serous cystadenocarcinoma), PAAD (Pancreaticadenocarcinoma), PCPG (Pheochromocytoma and Paraganglioma), PRAD(Prostate adenocarcinoma), READ (Rectum adenocarcinoma), SARC (Sarcoma),SKCM (Skin Cutaneous Melanoma), STAD (Stomach adenocarcinoma), TGCT(Testicular Germ Cell Tumors), THCA (Thyroid carcinoma), THYM (Thymoma),UCEC (Uterine Corpus Endometrial Carcinoma), UCS (UterineCarcinosarcoma), or UVM (Uveal Melanoma). These are referred to as the32 cancer subtypes throughout.

In preferred embodiments, the method comprises at least one gene,wherein the genes are those as enumerated herein. The method of claim 1where the at least one gene is selected among the list of genes ofclaims 66-96 to be dysregulated in the disease at hand.

In preferred embodiments, the tRF is selected among the 1,700 tRFs foundto be statistically significantly correlated with genes. In preferredembodiments, the gene is selected among the 12,509 genes found to bestatistically significantly correlated with tRFs.

In preferred embodiments, selection among the tRF's, where the tRF andthe gene are positively correlated. In certain embodiments, weadminister a therapeutic to raise the tRF's abundance. In otherembodiments, we wish to lower the tRF's abundance. Accordingly, weadminister a preparation comprising a therapeutic agent that lowers thetRF's abundance.

Alternatively, in some embodiments, where the tRF and the gene arenegatively correlated. Therefore, we may wish to lower the tRF'sabundance. Accordingly, we administer a preparation comprising atherapeutic agent that lowers the tRF's abundance. In other embodiments,we wish to raise the tRF's abundance and accordingly, we administer apreparation comprising a therapeutic agent that raises the tRF'sabundance.

In aspects where the tRF and gene are positively or negativelycorrelated, we may wish to do either lower the gene's abundance,accordingly, we administer a preparation comprising a therapeutic agentthat lowers the gene's abundance. Alternatively, we can raise the gene'sabundance, though a therapeutic. The method of claim 8 where we wish tolower the gene's abundance. Embodiments therefore raise or lower thegene abundance or raise or lower the tRF abundance.

In certain embodiments, the tRFs are considered in order of decreasingdesirability. For example, we can evaluate where a tRF's desirability isdecided by the number of tRF-gene correlations in which it participates.And methods can be applied where the genes are considered in order ofdecreasing desirability; for example, where a gene's desirability isdecided by the number of tRF-gene correlations in which it participates.

In certain embodiments, the gene abundance is lowered or raised byproxy.

In certain embodiments, the gene abundance of the gene in a correlatedor anticorrelated pair is directly lowered or directly raised.

In further embodiments, a method of treating a patient for a cancerthrough by focusing on disease-specific tRFs among those that arecorrelated with genes or by focusing on disease-specific genes amongthose that are correlated with tRFs corresponding to the disease in saidpatient comprising: selecting at least one of these disease-specifictRFs; selecting at least one of these disease-specific genes; andadministering a therapeutic composition sufficient to modify theabundance of the at least one disease-specific tRF or of the at leastone disease-specific gene.

The method wherein the disease type is ACC (Adrenocortical carcinoma)and the tRF and the tRF is selected from the group of sequencesconsisting of SEQ 1 through SEQ 31.

The method wherein the disease is BLCA (Bladder Urothelial Carcinoma)and the tRF is selected from the group of sequences consisting of SEQ 32through SEQ 40.

The method wherein the disease is BRCA (Breast invasive carcinoma) andthe tRF is selected from the group of sequences consisting of SEQ 41through SEQ 58.

The method wherein the disease is CESC (Cervical squamous cell carcinomaand endocervical adenocarcinoma) and the tRF is selected from the groupof sequences consisting of SEQ 59 through SEQ 71.

The method wherein the disease is COAD (Colon adenocarcinoma) and thetRF is selected from the group of sequences consisting of SEQ 72 throughSEQ 77.

The method wherein the disease is DLBC (Lymphoid Neoplasm Diffuse LargeB-cell Lymphoma) and the tRF is selected from the group of sequencesconsisting of SEQ 78 through SEQ 88.

The method wherein the disease is ESCA (Esophageal carcinoma) and thetRF is selected from the group of sequences consisting of SEQ 89 throughSEQ 94.

The method wherein the disease is HNSC (Head and Neck squamous cellcarcinoma) and the tRF is selected from the group of sequencesconsisting of SEQ 95 through SEQ 109.

The method wherein the disease is KICH (Kidney Chromophobe) and the tRFis selected from the group of sequences consisting of SEQ 110 throughSEQ 120.

The method wherein the disease is KIRC (Kidney renal clear cellcarcinoma) and the tRF is selected from the group of sequencesconsisting of SEQ 121 through SEQ 128.

The method wherein the disease is KIRP (Kidney renal papillary cellcarcinoma) and the tRF is selected from the group of sequencesconsisting of SEQ 129 through SEQ 132.

The method wherein the disease is LAML (Acute Myeloid Leukemia) and thetRF is selected from the group of sequences consisting of SEQ 133through SEQ 165.

The method wherein the disease is LGG (Brain Lower Grade Glioma) and thetRF is selected from the group of sequences consisting of SEQ 166through SEQ 180.

The method wherein the disease is LIHC (Liver hepatocellular carcinoma)and the tRF is selected from the group of sequences consisting of SEQ181 through SEQ 186.

The method wherein the disease is LUAD (Lung adenocarcinoma) and the tRFis selected from the group of sequences consisting of SEQ 187 throughSEQ 194.

The method wherein the disease is LUSC (Lung squamous cell carcinoma)and the tRF is selected from the group of sequences consisting of SEQ195 through SEQ 217.

The method wherein the disease is MESO (Mesothelioma) and the tRF isselected from the group of sequences consisting of SEQ 218 through SEQ235.

The method, wherein the disease is OV (Ovarian serouscystadenocarcinoma), and the tRF is sequence SEQ 236.

The method wherein the disease is PAAD (Pancreatic adenocarcinoma) andthe tRF is selected from the group of sequences consisting of SEQ 237through SEQ 242.

The method wherein the disease is PCPG (Pheochromocytoma andParaganglioma) and the tRF is selected from the group of sequencesconsisting of SEQ 243 through SEQ 251.

The method wherein the disease is PRAD (Prostate adenocarcinoma) and thetRF is selected from the group of sequences consisting of SEQ 252through SEQ 261.

The method wherein the disease is READ (Rectum adenocarcinoma) and thetRF is selected from the group of sequences consisting of SEQ 262through SEQ 270.

The method wherein the disease is SARC (Sarcoma) and the tRF is selectedfrom the group of sequences consisting of SEQ 271 through SEQ 275.

The method wherein the disease is SKCM (Skin Cutaneous Melanoma) and thetRF is selected from the group of sequences consisting of SEQ 276through SEQ 283.

The method wherein the disease is STAD (Stomach adenocarcinoma) and thetRF is selected from the group of sequences consisting of SEQ 284through SEQ 291.

The method wherein the disease is TGCT (Testicular Germ Cell Tumors) andthe tRF is selected from the group of sequences consisting of SEQ 292through SEQ 308.

The method wherein the disease is THCA (Thyroid carcinoma) and the tRFis selected from the group of sequences consisting of SEQ 309 throughSEQ 324.

The method wherein the disease is THYM (Thymoma) and the tRF is selectedfrom the group of sequences consisting of SEQ 325 through SEQ 331.

The method wherein the disease is UCEC (Uterine Corpus EndometrialCarcinoma) and the tRF is selected from the group of sequencesconsisting of SEQ 332 through SEQ 344.

The method wherein the disease is UVM (Uveal Melanoma) and the tRF isselected from the group of sequences consisting of SEQ 345 through SEQ372.

The method wherein the disease type is ACC (Adrenocortical carcinoma),and the gene is selected from the group consisting of: CSDC2,CSGALNACT1, RERG, PCMTD1, PLCB3, YEATS2, BIRC2, MVP, MYST3, ARL6IP5,TRANK1, TMEM45A, ACVR1, PGCP, VCL, MSRA, C10orf54, DCUN1D3, CTDSPL2,SIK2, TMCO6, SRCAP, TMEM159, PLEKHO2, HLA-E, TAX1BP3, C11orf75, RCE1,NDRG4, MR1, MARK2, FAM21B, HLA-B, RBL2, CABC1.

The method wherein the disease type is BLCA (Bladder UrothelialCarcinoma), and the gene is selected from the group consisting of:ACTG2, TGFBR3, PRELP, RERE, OSR1, TCEAL1, NNAT, GCOM1, MMP2, MYST4,SYNPO2, C16orf45, FYCO1, MYH11, CSRP1, MEIS2, ACTA2, CLU, LOXL1, IGFBP4,TXNIP, SLIT3, CHRDL2, MYL9.

The method wherein the disease type is BRCA (Breast invasive carcinoma),and the gene is selected from the group consisting of: CRTC1, CALCOCO1,SLC27A1, CROCC, PGPEP1, PSD4, TBC1D17, PHF15, ARAP1, TNFRSF14, NISCH,MED16, RGS12, MYO15B, AGXT2L2, RFX1, C21orf2, NEURL4, TPCN1, HOOK2,LTBP3, SPHK2, ABTB1, ABCD4, ZBTB48, CIRBP, CYTH2, ZNF446, PHF1, RPS9,MZF1, FAM160A2, KIF13B, GLTSCR2, WDR81, SH2B1, RHOBTB2, CRY2, LTBP4,HDAC7, ZNF219, MUM1, RBM5, RAPGEF3, CCDC9.

The method wherein the disease type is CESC (Cervical squamous cellcarcinoma and endocervical adenocarcinoma), and the gene is selectedfrom the group consisting of: NBPF10, JRK, SMG5, ALDH1A2, MFAP4, ZFYVE1,CDC42BPB, ENTPD4, IGF1, ZFYVE26, APOLD1, LOC200030, KIAA0430, UBN1,VASH1, RANBP10, WDR37, MGP, MON1B, CNN1, MAT2A, PGR, KIAA0100, C14orf21,HCFC1, LRP10, DIDO1, FBXL18, ATP6V0A1, RGS2, MLXIP, TRIM56, CTGF,KIAA0284, DES, SFRP4, PDPK1, TAOK2, SMCR8, CLN8, UNC119B, TRIM25,CYB561D1, TBC1D2B, DNAJC5, CRAMP1L, ZNF646, ZC3HAV1, KHNYN, PSKH1, RGS1.

The method wherein the disease type is COAD (Colon adenocarcinoma), andthe gene is selected from the group consisting of: ATP8B2, SYNE1, WIPF1,AOC3, LIMS1, FZD1, CYBB, MAFB, GIMAP6, REST, STAB1, FPR3, MSRB3, FRMD6,CALCRL, MPEG1, MYLK, ELTD1, FGL2, SPARCL1, PLXDC2, LAIR1, ITGB2, NRP1,MRC1, ZEB1, SYT11, NCKAP1L, AXL, APLNR, ZEB2, EDIL3, FERMT2, PTPRM,RASSF2, PKD2, PHLDB2, TCF4, IL1RA, HEG1, HIPK3, NEXN, TMEM140, AMOTL1,A2M, TIE1, AKT3, CD163, LPHN2, OSMR, CSF1R, DAAM2, IL1R1, GPC6, SLC8A1,FBN1, GNB4, GPNMB, DOCK2, KIAA1462, CSF2RB, MYO5A, S1PR1, ARHGEF6.

The method wherein the disease type is DLBC (Lymphoid Neoplasm DiffuseLarge B-cell Lymphoma), we found that the mRNAs of the following genessatisfy these criteria: GOLGA2, DCHS1, CLDND1, CSRNP2, FRMD8, SLCO2A1,ARHGAP23, NID1, DUSP7, TBC1D20, YAP1, WDR82, TMEM43, TJP1, CARD8,ZNF213, KIAA0232, EPAS1, VPS11, PHC1, SKI, DAG1, ANKRD40, FAT1, PHF12.

The method wherein the disease type is ESCA (Esophageal carcinoma), andthe gene is selected from the group consisting of: SSC5D, PDLIM3, CELF2,TIMP3, ABCC9, CALD1, COL8A1, GREM1, THBS4, PRUNE2, TMEM47, PBXIP1, PLN,CCDC80, C7, PODN, DDR2, PPP1R12B, MRVI1, LMOD1, C7orf58, HSPB7, TAGLN,PPP1R16B, GFRA1, LOC728264, SGCD, PGM5.

The method wherein the disease type is HNSC (Head and Neck squamous cellcarcinoma), and the gene is selected from the group consisting of:GABBR1, C14orf179, METT11D1, C6orf125, ZNF692, FKBP2, FAM113A,LOC388789, TAZ, WASH3P, CDK5RAP3, PLBD2, SDR39U1, CPT1B, UBL5, C14orf2,LOC146880, THAP3, ANKRD13D, C12orf47, ATP5E, ATPIF1, SYF2, C8orf59,WASH7P, NPIPL3, CDK10, C1orf151, MRPS21, C19orf60, C7orf47, CENPT, GAS5,KIFC2, NFIC, RPL39, UQCRB, COX6C, LUC7L, CCS, COMMD6, ZNF133, SNHG12,C11orf31, NPEPL1.

The method wherein the disease type is KICH (Kidney Chromophobe), andthe gene is selected from the group consisting of: BMPR1A, EXT1, TFAM,PDCD11, MTIF2, POLR3A, MAPK8, PRDX3, COQ5.

The method wherein the disease type is KIRC (Kidney renal clear cellcarcinoma), and the gene is selected from the group consisting of:ARHGAP19, KIAA1671, KIAA0754, KIAA1147, ZNF45, KLF13, MYO9A, FUT11,ASH1L, KIF13A, TUBGCP3, MTF1, FAM168A.

The method wherein the disease type is KIRP (Kidney renal papillary cellcarcinoma), and the gene is selected from the group consisting of:GLCCI1, CDK13, POGZ, UBN2, CREBZF, NPHP3, VEZF1, CHD1, YPEL2, LRRC37B2,GPATCH8, ENC1, TTC18, Cllorf61, RSBN1L, EFNB2, PHIP, RBAK, SPEN, RBM9,SMURF2, ZNF264, ZNF587, PTPN12, TPBG, RBM33, DMTF1, CCNT2, ARID4B,ARGLU1, CREB1, KIAA0753, BTAF1, C17orf85, RLF, MLL5, ZFC3H1, ZNF160,PRPF38B, SETD5, ARRDC4, HOOK3, RC3H1, MLL3, RNF207, MAP3K1, PLEKHH2,CCDC57, DAPK1, LUC7L3.

The method wherein the disease type is LAML (Acute Myeloid Leukemia),and the gene is selected from the group consisting of: SUPT5H, SKIV2L,IKBKG, HGS, MIB2, MED15, STK25, ANAPC2, RHOT2, SFI1, CUL9, ARHGEF1,GTPBP2, KIAA0892, MBD1, UCKL1, DHX16, ZFYVE27, APBA3, PI4 KB, C19orf6,SPSB3, CAPN10, FLYWCH1, ATG4B, CDC37, LZTR1, MAN2C1, C1orf63, DVL1,EDC4, DHX34, PCNXL3, EXOC3, FUK, FBXL6, LMF2, HDAC10, E4F1, TSC2,ZDHHC8, CPSF3L, FAM160B2, CLCN7, LRRC14, D2HGDH, ZNF335, FHOD1, SOLH,ZBTB17, POLRMT, SLC26A1, KIAA0415, SELO, SAPS2, NME3, KLHL36, SCYL1,USP19, DGKZ, CYHR1, ATG2A, VPS16, XAB2, ACTR5, ZNF76, ATP13A1, RNF31,GPN2, MUS81, FAM73B, TTC15, CXXC1, TRMT2A, WDR8, PTGES2, TELO2, RFNG,SLC39A13.

The method wherein the disease type is LGG (Brain Lower Grade Glioma),and the gene is: EXD3.

The method wherein the disease type is LIHC (Liver hepatocellularcarcinoma), we found that the mRNA of the following gene satisfies thesecriteria: DYRK2.

The method wherein the disease type is LUAD (Lung adenocarcinoma), andthe gene is selected from the group consisting of: CROCCL1, RHPN1,ABCA7, RGL3, PDXDC2, ENGASE, ATG16L2, CSAD, TTLL3, ARHGEF12, ANKS3,LOC100132287, SGSM2, HEXDC, LPIN3, ACCS, PLEKHMIP, ANO9, ELMOD3,KIAA0895L, AP1G2, ACAP3, ECHDC2, NXF1, JMJD7-PLA2G4B, TMEM175, CCDC64B,ANKMY1.

The method wherein the disease type is LUSC (Lung squamous cellcarcinoma), and the gene is selected from the group consisting of: PKD1,CHKB-CPT1B, WDR90, MACF1, RBM6, LENG8, TAF1C, COL16A1, CAPN12, RBM39,ACIN1, FNBP4, PILRB, DMPK, SFRS5, AHSA2, RBM25, PLCG1, SNRNP70,NCRNA00201, GIGYF1, SRRM2, GOLGA8B, ZGPAT, RTEL1, COL27A1, MAPK8IP3,PABPC1L, HSPG2, AKAP13, LRP1, NKTR, ATAD3B, TUBGCP6, ZNF276, MICALL2,CLCN6, NSUN5P2, NEAT1, LAMA5, CHD2, PPP1R12C, FAM193B, NPIP, CDK11A,STX16, LTBP2, LOC91316, NBEAL2, FLJ45340, LRDD, CCDC88B, GOLGA8A.

The method wherein the disease type is MESO (Mesothelioma), and the geneis selected from the group consisting of: C15orf40, SNUPN, CHTF8,CLNS1A, CSNK1D, DPF2, PCIF1, DNAJC4, SECISBP2, C5orf32, RPRD1B, RPL38,NDUFA10, RPRM, BAT4, RSL1D1.

The method wherein the disease type is OV (Ovarian serouscystadenocarcinoma), and the gene is selected from the group consistingof: KIAA0907, ULK3.

The method wherein the disease type is PAAD (Pancreatic adenocarcinoma),and the gene is selected from the group consisting of: NUAK1, KBTBD4,HMCN1, DSTYK.

The method wherein the disease type is PCPG (Pheochromocytoma andParaganglioma), and the gene is selected from the group consisting of:CACNA2D1, TP53BP1, KIAA1244, MARCH8, PCDHGC4, ESYT2, DHX15, TECPR1,MAP3K2, TBC1D24, PCDH1, IPO11, MGAT5, TRAM2, ADAM10, GNA11, CBX6, SNURF,RIF1, CNTN1, LMBRD2, CAND1, TRIP12, RC3H2, PAK3, TMCO3, CSNK2A1P, ASB1,AKAP2, ROCK2, NUP155, PIK3C3, KLHDC10, RAB35, GTF2I, HSPA8, FAM49A.

The method wherein the disease type is PRAD (Prostate adenocarcinoma),and the gene is selected from the group consisting of: FEM1B, TGOLN2,SEPT9, MYOCD, LUZP1, TLN1, PIAS1, RNF111, DCBLD2, URB1, ZBTB40, ZNF516,ATXN1L, RHBDD1, HUWE1, VPS13D, ITPR1, NNT, ERC1.

The method wherein the disease type is READ (Rectum adenocarcinoma), andthe gene is selected from the group consisting of: FZD4, CD93, DYNC1I2,ENG, ELK3, KDR, FAM101B, PXDN, GIMAP4, F13A1, VCAM1, ARHGAP31, CD34,GNG2, LCP2, VWF, CSF1, GPR116, KIRREL, MMRN2, ETS1, ITGA4, FAM120B.

The method wherein the disease type is SARC (Sarcoma), we found that themRNAs of the following genes satisfy these criteria: SH3BGRL, SORBS1,MAPK4, LYNX1, MICAL3, AKAP1, LIMS2, RNF38, LOC283174, CAND2, PLIN4,MOAP1, RNF19A, RABGAP1, C5orf4, FRY, ARHGEF17, SETMAR, SSH3, NUMA1,PBX1, TOR1AIP1, TACC2, RAB3D, BBS1, CEP68, GPRASP1, SVIL, CRBN, CRTC3,ZFYVE21, SLMAP, RASL12, SCAPER, STAT5B, ZAK, EZH1.

The method wherein the disease type is SKCM (Skin Cutaneous Melanoma),and the gene is selected from the group consisting of: MLL4, LMTK2,APBB3, C17orf56, LOC388796, ANO8.

The method wherein the disease type is STAD (Stomach adenocarcinoma),and the gene is selected from the group consisting of: KCNMA1, MAST4,FHL1, ATP2B4, TENC1, C20orf194, ASB2, C10orf26, TTC28, FAM13B, ITPKB,GNAO1, FAM129A, ZBTB4, FNBP1, FLNA, CCDC69, STON1, NFASC, PAPLN, ADCY5,LPP, NEGR1, ABI3BP, INPP5B, TNXB, ANGPTL1, ANK2, EPHA3, ZCCHC24, SETBP1,PRICKLE2, LTBP1, RGMA, DARC, KANK2, SYNPO.

The method wherein the disease type is TGCT (Testicular Germ CellTumors), and the gene is selected from the group consisting of: ATF6,CTDSP2, MKL2, TEAD1, ZNF407, TRAPPC9, AHDC1, LANCL1, KCTD20, OXR1, SNX1,CSNK1G1, KIAA0247, LDOC1L, EPC1, GRLF1, ABHD2, RAIl, ARID1B, ITFG1, MUT,KIAA1737, LAMA2, KIAA1109, CCNI, DIXDC1, C6orf89, RNF144A, APPBP2,KLF12, ZFP91.

The method wherein the disease type is THCA (Thyroid carcinoma), and thegene is selected from the group consisting of: KIAA0495, PHF21A, ZBTB5,SFRS6, NCOA5, ZNF814, IP6K2, IFT140, INTS3, ZNF559, SETD4, TGIF2, VILL,KCNC3, UBE2G2, FBXO9, IPW, DUOXA1, CACNA2D2, EFHC1, FAM189A2,GTF2IRD2P1, KIAA1683, AP4B1, SCAND2, CDRT4, UNKL, NYNRIN, ARMC5,MAPKBP1, USP40, VEGFA, OLFM2, FBF1, TCF7L1, MXD4, IKBKB, POFUT2, BOC,TCF7L2, RMST, TRO.

The method wherein the disease type is THYM (Thymoma), and the gene isselected from the group consisting of: ZFYVE9, LOC399959, SIX1, PDGFC.

The method wherein the disease type is UCEC (Uterine Corpus EndometrialCarcinoma), and the gene is selected from the group consisting of: RNMT,SON, MDN1, CELF1, RIPK1, YLPM1, XRN2, BPTF, RQCD1, PAFAH1B2, BOD1L,SBNO1, RNF169, PIK3R4, LRCH3, DCP1A, SF3A1, SLC9A8, TNRC6A, BRPF3.

The method wherein the disease type is UCS (Uterine Carcinosarcoma), wefound that the mRNA of the following gene satisfies these criteria:RHBDF1.

The method wherein the disease type is UVM (Uveal Melanoma), and thegene is selected from the group consisting of: MAP1A, TCIRG1, ECM1,C14orf159, WARS, PCYOX1L.

A further embodiment is directed towards a method of treating a diseasein a patient comprising: determining the disease type that is ailing thesaid patient; determining at least one gene whose abundance isdysregulated in the said patient; determining at least one tRF that iscorrelated with the at least one dysregulated gene; and administering atherapeutic composition sufficient to modify the abundance of the atleast one mRNA corresponding to a gene whos abundance is dysregulated inthe said patient. Preferably, the disease is detected by taking a samplefrom a patient. An appropriate therapeutic composition may be anycomposition targeted to the regulation of the abundance of the at leaston mRNA corresponding to a gene, for example a small molecule, abiologic, an antibody, an antimer, other other material known to thoseof ordinary skill in the art.

A further embodimner is directed towards a method of treating a diseasein a patient comprising: selecting at least one tRF that is transcribedin the ailing tissue and of the said patient; selecting at least onegene that is correlated with the at least one tRF that is transcribed inthe ailing tissue of the said patient; and administering a therapeuticcomposition sufficient to modify the abundance of the at least one gene.

In certain embodiments, a further step comprises collecting an ailingtissue sample from said patient.

In certain embodiments, as described herein, wherein at least one tRF isselected based upon having at least 20 different mRNA correlations forat least the disease being treated, and having no more than 3 differentmRNA correlations for another disease not being treated.

In certain embodiments, as described herein, wherein at least one mRNAis selected based upon having at least 20 different tRF correlations forat least the disease being treated, and having no more than 3 differenttRF correlations for another disease.

A further embodiment is directed towards a method of treating a patientfor a disease of interest by focusing on disease-specific tRFs amongthose that are correlated with genes or by focusing on disease-specificgenes among those that are correlated with tRFs comprising: compilingstatistically-significant correlations between tRFs and the mRNAs ofgenes by analyzing datasets from two or more diseases, one of which isthe disease of interest; determining for each tRF the number ofcorrelations with mRNAs in which it participates in the disease ofinterest; determining for each tRF the number of correlations with mRNAsin which it participates in the one or more diseases other than thedisease of interest; subselecting only those tRFs that participate in ahigh number of correlations with mRNAs in the disease of interest and ina low number of correlations with mRNAs in the one or more diseasesother than the disease of interest; determining for each mRNA the numberof correlations with tRFs in which it participates in the disease ofinterest; determining for each mRNA the number of correlations with tRFsin which it participates in the one or more diseases other than thedisease of interest; subselecting only those mRNAs that participate in ahigh number of correlations with tRFs in the disease of interest and ina low number of correlations with tRFs in the one or more diseases otherthan the disease of interest; choosing at least one of the subselectedtRFs; choosing at least one of the subselected mRNAs; and administeringa therapeutic composition sufficient to modify the abundance of the atleast one chosen tRF or of the at least one chosen mRNA. In preferredembodiments, the disease of interest is cancer. Preferably, where atleast one of the one or more diseases other than the disease of interestis cancer. In preferred embodiments, where for each tRF the number ofcorrelations with mRNAs in the disease of interest is at least two timeshigher than the average number of correlations that this tRF has withmRNAs in the one or more diseases other than the disease of interest. Inpreferred embodiments, where for each tRF the number of correlationswith mRNAs in the disease of interest is at least three times higherthan the average number of correlations that this tRF has with mRNAs inthe one or more diseases other than the disease of interest. Inpreferred embodiments, where for each tRF the number of correlationswith mRNAs in the disease of interest is at least four times higher thanthe average number of correlations that this tRF has with mRNAs in theone or more diseases other than the disease of interest. In preferredembodiments, where for each mRNA the number of correlations with tRFs inthe disease of interest is at least two times higher than the averagenumber of correlations that this mRNA has with tRFs in the one or morediseases other than the disease of interest. In preferred embodiments,where for each mRNA the number of correlations with tRFs in the diseaseof interest is at least three times higher than the average number ofcorrelations that this mRNA has with tRFs in the one or more diseasesother than the disease of interest. In preferred embodiments, where foreach mRNA the number of correlations with tRFs in the disease ofinterest is at least four times higher than the average number ofcorrelations that this mRNA has with tRFs in the one or more diseasesother than the disease of interest.

A further embodiment is directed towards a method of identifying atarget for disease treatment by focusing on disease-specific tRFs amongthose that are correlated with genes or by focusing on disease-specificgenes among those that are correlated with tRFs comprising: compilingstatistically-significant correlations between tRFs and the mRNAs ofgenes by analyzing datasets from two or more diseases, one of which isthe disease of interest; determining for each tRF the number ofcorrelations with mRNAs in which it participates in the disease ofinterest; determining for each tRF the number of correlations with mRNAsin which it participates in the one or more diseases other than thedisease of interest; subselecting only those tRFs that participate in ahigh number of correlations with mRNAs in the disease of interest and ina low number of correlations with mRNAs in the one or more diseasesother than the disease of interest; determining for each mRNA the numberof correlations with tRFs in which it participates in the disease ofinterest; determining for each mRNA the number of correlations with tRFsin which it participates in the one or more diseases other than thedisease of interest; subselecting only those mRNAs that participate in ahigh number of correlations with tRFs in the disease of interest and ina low number of correlations with tRFs in the one or more diseases otherthan the disease of interest; choosing at least one of the subselectedtRFs; choosing at least one of the subselected mRNAs; and, wherein thecorrelated tRF's or one or more mRNAs are the targets for treatment. Incertain embodiments, we further administer a therapeutic compositionsufficient to modify the abundance of the at least one chosen tRF or ofthe at least one chosen mRNA.

In summary, tRFs and the uncovered associations in which theyparticipate provide an excellent framework in which to develop targetedtherapeutics and realize “precision” medicine.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1C tRF distributions by category, length, and abundance. (A)Length distributions of tRFs broken down by type and organelle in 10 ofthe 32 analyzed cancer types (for each length the mean across samplesand the standard error are shown). Each category has a unique andcancer-type-specific distribution. (B) Heatmap and hierarchicalclustering (metric: Kendall's tau coefficient) of the expressionprofiles of the structural categories per origin as the sum of tRFexpression that fall in each category. Mitochondrial tRFs are clusteredtogether while nuclear tRFs are split by length. Short nuclear tRFs (<24nt) are clustered together with the MT tRFs in the bottom half of theheatmap. Longer nuclear tRFs are clustered together in the top half ofthe heatmap separately from MT tRFs and short nuclear tRFs. This heatmaphighlights the observation that tRFs are diverse. (C) A PCA plot showingthe clustering for various combinations of tRF type, length, and genomeorigin, similar to the clustering shown in (B). The correspondencebetween tRF length and shading of the fill of the used marker is thesame for both panels (B) and (C).

FIGS. 2A-2B Isoacceptor representation among the tRFs. (A) Heatmap andhierarchical clustering (metric: Euclidean distance) of the abundanceprofile of each isoacceptor, calculated as the sum of the expression oftRFs it produces, in all 32 cancers. Nucleus-encoded isoacceptors aredenoted on the side color bar using white, whereas MT ones are denotedin black. (B) Box-plots showing the percentage expression of tRFs fromspecific isoacceptors across BRCA and UCEC samples. As can be seen, thetop tRF-producing isoacceptors differ in the two cancers. Thehighest-expressed isoacceptor in BRCA is the nuclear tRNA^(GlyGCC)whereas in UCEC it is the MT tRNA^(ValTAC) isoacceptor.

FIGS. 3A-3B Cleavage points across the tRFs. (A) A schematic that showsthe preferences of the 5′ termini (white pentagon arrows) and 3′ termini(gray chevrons) for 5′-tRFs, 3′-tRFs, and i-tRFs. For clarity purposes,separate schematics show the preferences of the 5′ termini and the 3′termini for i-tRFs. The thickness of the arrow or chevron indicates thepreference for the corresponding position in a qualitative manner.Groups of arrows are tagged with black-on-gray labels whereas groups ofchevrons are tagged with black-on-white labels. The capital X of eachlabel indicates the terminal nucleotide, either 5′ or 3′: this letter Xis preceded (followed, respectively) by the three most frequentdinucleotides found immediately upstream (downstream, respectively) inthe mature tRNA for the most abundant tRFs that begin or end at theposition. Squares containing white circles indicate positions with knownmodifications. We stress that these modifications are shown forreference purposes only as it is unclear whether they occur in thetissues and tissue states that are represented by the TCGA datasets weanalyzed. (B) Box plots showing the preferences for the starting (left)and ending (right) positions for i-tRFs in LUAD and OV.

FIG. 4 His(−1U) fragments. Abundance ratios of uridylated His(−1)5′-tRFs from nuclear tRNA^(HisGTG) that end at consecutive positionswithin the mature tRNA. The shown ratios for normal (gray) and cancer(black) samples represent 2,635 tumor and samples from six TCGA cancers:BLCA, ESCA, PAAD, BRCA, LUAD, and SKCM. Values are shown only forstatistically significant tRFs. Y-axis: log₂. At X=i, we plot the ratio“log₂ (mean [(RPM of 5′-tRF ending at position i)/(RPM of 5′-tRF endingat position i+1)]).” Here, RPM stands for reads-per-million.

FIGS. 5A-5C Correlations, Compartments and Repeats. (A) Heatmap andhierarchical clustering (metric: Euclidean distance) depicting thefraction of the 32 cancer types in which the shown 58 isoacceptors(rows) are anti-correlated with the listed GO terms (columns). Thedescriptions for the shown GO terms appear in Supp. Table S6. The sameisoacceptors correlated negatively with the same pathways, but not atthe gene or tRF level across cancers (FIG. 14 ). (B) A map showing thelocalization of the protein products whose mRNAs have positive ornegative statistically significant correlations with tRFs. Forcomparison purposes, we included the localization of the proteinproducts whose mRNAs have positive or negative statistically significantcorrelations with miRNA isoforms (isomiRs), which we have discussed inprevious work (66,71,72). The maps are shown separately for nuclear andMT tRFs and separately for positive and negative correlations. The sizeof the shown squares corresponds to the number of protein products thatlocalize in the shown compartment. The color of the box representsenrichment (black; Z-score≥2) or depletion (light gray; Z-score≤−2)compared to the expected distribution. (C) Heatmap and hierarchicalclustering (metric: Pearson correlation) showing the statisticalsignificance (Z-score) of the enrichment or depletion of fragments fromrepeat categories in the genomic loci of mRNAs that are anti-correlatedwith tRFs. Enrichments and depletions are shown separately for theunspliced mRNA (panel A), in the intronic regions (panel B), or in theexonic regions (panel C) of the mRNAs that are positively or negativelycorrelated with tRFs in each cancer type. Panels B and C maintain thesame order of cancers (rows) and repeat elements (columns) as theyappear in the heatmap of panel A. The barplots to the right of theheatmaps show the number of cancer types in which each family is foundenriched/depleted. A representative list of the tRF-mRNA pairs (bothpositively-correlated and negatively-correlated), as well as arepresentative list of the tRF-tRF pairs (both positively-correlated andnegatively-correlated) for the case of breast cancer (BRCA) appears inSupp. Table S4. A complete list of all tRF-tRF and tRF-mRNA correlations(both positive and negative) for the remaining TCGA cancers can begenerated as described below and as described under “CorrelationAnalyses” in the Methods section. The latter table also lists thecomplete list of tRF-tRF pairs (both positively-correlated andnegatively-correlated), again separately for each cancer.

FIG. 6A-E tRF correlations in patients of different sex. Example showingthe dependence of the tRF profiles on the sex of patients with lungadenocarcinoma. Shown are the networks of tRF-tRF correlations that aresupported LUAD samples from TCGA that belong to either men or women (A),the sub-network of correlations that are present exclusively in samplesfrom male LUAD patients (B), the sub-network of correlations that arepresent exclusively in samples from female LUAD patients (C), and,finally, the sub-network of correlations that are present in LUADpatients of both sexes (D). Various combinations of shapes andfills/shading capture the source tRNA isoacceptor, the tRFs' structuralcategory, the tRFs' length, and the tRFs' genomic origin (nuclear orMT). Edges between nodes correspond to a Spearman correlation ≤−0.5(negative correlations) and have an associated FDR≤0.01.

FIGS. 7A-7C v2.0 of MINTbase. We have augmented the interface ofMINTbase to enable interactive and detailed exploration of the tRFscontained in it. Here we show three of the four histograms and otherinformation that is available in the record of the His(−1) 5′-tRFTGCCGTGATCGTATAGTGGTT from tRNA^(HisGTG) (note the starting “T”). Seetext for more details.

FIG. 8 Correlations between tRFs and the mRNAs of genes belonging to thesignal transduction pathway known as “the PI3K-AKT pathway,” in colonadenocarcinoma (COAD) and pancreatic adenocarcinoma (PAAD). Thesolid-line-contour rectangle shows tRFs that are correlated withPI3K-AKT pathway mRNAs in COAD but not in PAAD. The dashed-line-contourrectangle shows the analogous information for PAAD. Thecompound-line-contour rectangle shows tRFs that are present in andcorrelated with PI3K-AKT pathway mRNAs in COAD and in PAAD. Note,however, that these tRFs maybe correlated with different mRNAs from thispathway in each cancer and the correlations may have different signs ineach cancer, e.g. the tRF:mRNA correlation could be positive in COAD andnegative in PAAD.

FIGS. 9A-9B Examples of protein complexes whose mRNAs are correlatedwith tRFs in at least three different cancer types. (A) Oxidativephosphorylation. (B) Ribosome-linked proteins.

FIGS. 10A-10C Members of the integrin family of proteins associate withtRFs and isomiRs in a cancer-dependent manner. (A) The case of ovarianserous adenocarcinoma (OV). (B) The case of brain lower grade glioma(LGG). (C) The case of acute myeloid leukemia (LAML). Note that in allthree cases, we show the same underlying network of protein-proteininteractions involving integrins. The nodes labeled with the names ofmiRNA loci are meant to represent one or more isomiRs from thecorresponding locus that are correlated with the corresponding mRNAs.The nodes labeled with the names of tRNA isoacceptors are meant torepresent one or more tRFs from the tRNA template that are correlatedwith the corresponding mRNAs.

FIGS. 11A-11B Sex-based disparities in bladder urothelial carcinoma(BLCA). (A) Plot showing the number of correlations that the tRFs fromeach isoacceptor have with mRNAs in each sex in primary tumors of BLCA.The points are denoted with black-filled circles (respectively,white-filled circles) if there are ≥2× correlation in males(respectively, females). Note that this is a log₂-log₂ plot. (B)Protein-Protein interaction network showing cyclin-dependent kinases(CDK), and proteins interacting with CDKs, and the tRFs that interactdirectly with them reveal differential correlations between male andfemale BLCA patients. CDKs that are not differentially co-expressedbetween male and female patients are shown as gray circles.

FIG. 12 NMF clustering of samples using the tRF abundance profiles ineach cancer type's datasets. Here we show the results obtained for eachcancer by applying the NMF method to the tRF abundance profiles. Foreach of the 32 analyzed cancer types, we show the results for k=1, 2, 3,. . . , 10 clusters.

FIG. 13 Distribution of tRF abundance as a function of length and tRFtype. Plots showing the distributions of the abundances of the varioustRFs as a function of length. The plots are shown separately for each of32 TCGA cancer types. For each point on a curve a standard error isshown that is derived by analyzing the samples of the correspondingcancer type.

FIG. 14 Location and features of cleavage points. Plots showing how theidentified tRFs are distributed along the length of mature tRNAs. Theresults are shown separately for each tRF structural type and separatelyfor the tRFs' 5′ and 3′ termini. Moreover, the plots show the relativeposition of the tRFs with regard to recognizable landmarks along themature tRNA including: its 5′ end; the 5′ end of the D-loop; the 3′ endof the D-loop; the 5′ end of the A-loop; the 3′ end of the A-loop; the5′ end of the T-loop; the 3′ end of the T-loop; and, the mature tRNA's3′ end. In each case, we report additionally the dinucleotidecomposition of the mature tRNA at the position at hand. In all cases,the plots are reported in two ways: considering normalized abundance ofthe tRFs; and, considering the diversity of the tRFs independent oftheir abundance. The plots are shown separately for each of 32 TCGAcancer types.

FIG. 15 His(−1)T ratios across TCGA. Plots showing the ratio of His(−1T)tRFs whose 3′-termini end at consecutive position on the maturetRNA^(HisGTG). At abscissa x=i, we plot the value of the ratio log₁₀(mean (RPM abundance of His(−1T) tRF ending at position i/RPM abundanceof His(−1T) tRF ending at position i+1)). Grey curves (when present)represent ratios for normal samples. Black curves represent ratios fortumor samples. The grey and black curves are offset slightly withrespect to one another to enable comparisons.

FIG. 16 Jaccard/tRFs vs. tRFs across cancers. Even though any twocancers express largely the same collection of tRFs, the subset of tRFsthat are either correlated or anti-correlated with one another differscharacteristically across cancers. (A) A heatmap and hierarchicalclustering showing what portion of the tRFs that were considered for thecorrelation analyses in a given cancer are shared between cancer type Xand cancer type Y (B) A heatmap and hierarchical clustering showing whatportion of the tRFs that participate in statistically significantcorrelations or anti-correlations with tRFs in a given cancer are sharedbetween cancer type X and cancer type Y.

FIG. 17 Jaccard/tRFs vs. mRNAs and miRNAs vs mRNAs across cancers. Eventhough any two cancers express largely the same collection of tRFs,miRNAs and mRNAs, the subset of tRFs and miRNAs that are anti-correlatedwith mRNAs differs characteristically across cancers. (A-B) Heatmaps andhierarchical clusterings (metric: Euclidean distance) showing whatportion of the miRNAs (A) or mRNAs (B) that were considered for thecorrelation analyses are shared between any two cancer types. (C)Heatmap and hierarchical clustering (metric: Euclidean distance) showingwhat portion of the tRFs that are anti-correlated with mRNAs in cancertype X are also anti-correlated with mRNAs in cancer type Y (D) Heatmapand hierarchical clustering (metric: Euclidean distance) showing whatportion of the miRNAs that are statistically significantlyanti-correlated with mRNAs in cancer type X are also anti-correlatedwith mRNAs in cancer type Y (E) Heatmap and hierarchical clustering(metric: Euclidean distance) showing what portion of the mRNAs that arestatistically significantly anti-correlated with tRFs or miRNAs incancer type X are also anti-correlated with tRFs or miRNAs in cancertype Y.

FIG. 18 Distributions across cancers. (A) Distribution of the number ofcancers containing an enriched GO BP term that is supported by tRF-mRNAanti-correlations. (B) Distribution of the number of cancers containingan enriched GO BP term that is supported by miRNA-mRNAanti-correlations. (C) Jaccard index for the BP terms enriched for eachcancer. (D) Jaccard index among the BP terms showing the overlap inmRNAs/genes. For the construction of the dendrograms in (C) and (D),Euclidean distance was used as a metric.

FIG. 19 tRF correlations in TNBC patients of different race. Example ofrace dependence of tRF correlations in triple negative breast cancer.Shown are the networks of tRF-tRF correlations that are supported by allTNBC samples from TCGA, the sub-network of correlations that are presentexclusively in samples from Wh TNBC patients, the sub-network ofcorrelations that are present exclusively in samples from B/Aa TNBCpatients, and, finally, the sub-network of correlations that are presentin TNBC patients of both races. From left to right, the networks arecolor-coded by source tRNA, structural category, length, and nuclear/MTorigin. Edges between nodes correspond to a Spearman correlation ≥0.5(positive correlations) and have an associated FDR≤0.01. See also text.

FIG. 20 Tracking the origin of non-exclusive tRFs. (A) Scatter plot ofthe enrichment (or depletion) of non-exclusive tRFs among nuclear tRFsthat participate in correlation pairs with mRNAs. Shown on X-axis is theZ-score of the observed percentage of non-exclusive tRFs as compared tothe distribution of percentages of randomly chosen sets of tRFs.Accordingly, the Y-axis is the Z-score for the enrichment (ordepletion). Each dot represents a specific correlation type (i.e.negative or positive) for each of the 32 cancer types. Dashed lines marka Z-score threshold of +2 and −2. (B) Examples of overlap of instancesof non-exclusive tRFs with genes. Of all 32 cancer types, and thenumerous tRFs and mRNAs that were analyzed, these are the only pairsthat we found to be significantly correlated.

DETAILED DESCRIPTION OF THE EMBODIMENTS

Overall explanation of functionality.

While we mined tRFs from all 11,198 datasets, we included in ouranalyses only the 10,274 datasets that have been “white-listed” by thevarious consortia of The Cancer Genome Atlas (TCGA): these datasetscontain no special annotations in the associated clinical metadata (asof Oct. 28, 2015). Across the 32 cancer types, we identified 20,722distinct tRFs, 29% of which arise from MT tRNAs. The majority of thesefragments belong to the novel category of i-tRFs, i.e. they are whollyinternal to the mature tRNAs. The abundances and cleavage patterns ofthe identified tRFs depend strongly on cancer type. Of note, in all 32cancer types, we find that tRNA^(HisGTG) produces multiple and abundant5′-tRFs with a uracil at the −1 position, instead of the expectedpost-transcriptionally-added guanosine. Strikingly, these −1U His5′-tRFs are produced in ratios that remain constant across all analyzednormal and cancer samples, a property that makes tRNA^(HisGTG) uniqueamong all tRNAs. We also found numerous tRFs to be negatively correlatedwith many messenger RNAs (mRNAs) that belong primarily to four universalbiological processes: transcription, cell adhesion, chromatinorganization and development/morphogenesis. However, the identities ofthe mRNAs that belong to these processes and are negatively correlatedwith tRFs differ from cancer to cancer. Notably, the protein products ofthese mRNAs localize to specific cellular compartments, and do so in acancer-dependent manner. Moreover, the genomic span of mRNAs that arenegatively correlated with tRFs are enriched in multiple categories ofrepeat elements. Conversely, the genomic span of mRNAs that arepositively correlated with tRFs are depleted in repeat elements. Thesefindings suggest novel and far-reaching roles for tRFs and indicatetheir involvement in system-wide interconnections in the cell.

Results

The tRFs we discovered from all 11,198 datasets of TCGA are available athttps://cm.jefferson.edu/tcga-mintmap-profiles. Our analyses focus onlyon tRFs whose sequences fully overlap a mature tRNA. These tRFs canbelong to one of five structural categories (5′-tRFs, i-tRFs, 3′-tRFs,5′-tRHs and 3′-tRHs). They can also belong to two categories (exclusiveand ambiguous) based on their potential genomic origin. In terms oflength, all generated tRFs range from 16 to 30 nucleotides (nt). SeeMethods.

A Multitude of tRFs Across the 32 TCGA Cancer Types

We used our recently developed Threshold-seq algorithm (21) toautomatically determine a support threshold for each of the analyzeddatasets. Threshold-seq adapts to a dataset's depth of sequencing whilebeing immune to the potential presence of outliers, making it ideal forthis purpose. We report tRFs that exceeded Threshold-seq's recommendedthreshold in at least one of the analyzed datasets. For the range 16-30nt, we find a total of 20,722 distinct tRFs that exceed threshold. ThesetRFs comprise 1,717 5′-tRFs, 16,133 i-tRFs, 2,840 3′-tRFs, and 325′-tRHs. We note that fragments with lengths larger than 27 nt could betruncated versions of tRFs longer than 30 nt (see Methods). 18,453 ofthe 20,722 tRFs have lengths between 16 and 27 nt inclusive (=1,3955′-tRFs, 14,478 i-tRFs, 2,574 3′-tRFs, and six 5′-tRHs). We note thati-tRFs are abundant and very diverse, in agreement with our earlierfindings (4,8,22). Of the 20,722 tRFs, 13,904 (67%) are exclusive totRNA space whereas the remaining 6,818 have ambiguous genomic origin.For more detailed information, see Supp. Table S1.

We also adopted the approach of the TCGA working groups and carried outNMF clustering of the datasets in each of the 32 cancer types using tRFprofiles instead of miRNA profiles (see Methods). FIG. 12 summarizes theresults of the 288 NMF runs (SKCM samples were split into two typeswhereas GBM was excluded—see Methods).

Nuclear and MT tRFs Exhibit Distinct and Cancer-Dependent Profiles

In previous work, we showed differences in the length and abundanceprofiles of nucleus-encoded vs. MT-encoded tRFs in healthy individualsfrom the 1,000 Genomes Project (1KG) (4), breast (4) as well as prostatecancer (8) and liver cancer patients (22) that were not part of the TCGAinitiative. These results suggest that different cancer types exhibitdifferent distributions of nucleus-encoded and MT-encoded, respectively,tRFs. Thus, we sought to examine these profiles across all TCGA cancertypes.

FIG. 1A shows characteristic examples of the length distributions fornucleus- and MT-encoded tRFs and for 10 of the 32 cancer types: AAC,HNSC, LAML, OV, SKCM, THCA, TGCT, UCS, UCEC, and UVM. For a detaileddistribution of the different tRF categories in each of the 32 cancertypes see FIG. 13 and Supp. Table S2. All distributions show normalizedabundances in RPM. There are evident differences in the tRFs' structuraltype, lengths, nuclear vs. MT origin, and relative abundances. Forexample, in ACC and UVM, MT tRNAs are sources of comparatively moreabundant 5′-tRFs with lengths 20, 23, and 26 nt. Analogously, 30-merproxies from nuclear tRNAs are the most abundant species in almost all10 cancers. Also, in SKCM and OV, nuclear tRNAs are much strongercontributors of 3′-tRFs with length 18 nt, when compared to the othereight cancer types. FIG. 1B provides a global view of the structuralcategories (5′-tRFs, i-tRFs, and 3′-tRFs) and abundances of thepopulations of tRFs arising from nucleus-encoded and MT-encoded tRNAsacross cancer types. The Figure makes the considerable diversity ofthese molecules clear. FIG. 1C is a Principal Component Analysis (PCA)plot based on the same data and shows clear clusters for variouscombinations of tRF type, length, and genome of origin, corroboratingthe results of FIG. 1B. Note that the clusters are explained by tRFlength and tRF origin. Specifically, shorter molecules (usually ≤23 nt)are clustered together, separately from longer ones (usually ≥24 nt);additionally, there is clear distinction between tRFs originating in thenucleus from those originating in the MT. These findings indicate thatthe nuclear and MT tRNAs produce distinctly different populations oftRFs that depend on cancer type.

Isoacceptors Produce tRFs in a Cancer-Dependent Manner

Having established that both nuclear and MT tRNAs are prolific producersof tRFs, we sought to investigate how different tRNA isoacceptorscontribute to the abundance profiles of tRFs. We hypothesized that theproduction of tRFs per isoacceptor is cancer-dependent. To investigatethis, we computed the total normalized expression (in RPM) of tRFs thatoriginate from each isoacceptor. We did so separately for each of the 32cancer types and for each of the 61 nuclear and 20 MT anticodons. Thisallowed us to tag each isoacceptor with the level of expression of itstRFs on a per cancer basis. We then carried out hierarchical clusteringand generated the heatmap of FIG. 2A. Four isoacceptor groups areevident in this Figure. Three isoacceptors, the mitochondrialtRNA^(ValTAC) and the nuclear tRNA^(HisGTG) and tRNA^(GlyGCC), stand outas producing highly-abundant tRFs in all 32 cancer types.

From a cancer standpoint, we note that ACC, LAML, SKCM, UVM, COAD andREAD form a distinct branch of the dendrogram (FIG. 2A). All four ofthese cancers produce abundant tRFs from nearly all of the shownisoacceptors (see FIG. 1 for comparison), albeit with significantexpression variation. FIG. 2B highlights the variation in tRF expressionacross different cancers with the help of BRCA and UCEC. In BRCA, fourisoacceptors, tRNA^(GlyGCC(n)), tRNA^(ValTAC(mt)), tRNA^(HisGTG(n)), andtRNA^(GlnTTG(n)) produce most of the tRFs. On the other hand, in UCEC,it is tRFs from tRNA^(ValTAC(mt)), tRNA^(ArgTCG(n)), tRNA^(GlyGCC(n)),and tRNA^(HisGTG(n)) that are expressed abundantly. These findingsindicate that the production of tRFs is cancer-type-specific.

The Patterning of tRFs Depends on tRF Category, Isoacceptor, and CancerType

In light of the results of the previous section and the dependencies oftRF abundance on cancer type and isoacceptor, we sought to identifycancer-type specific tRNA cleavage patterns. Towards this end, weanalyzed “where” tRFs are located with respect to the mature tRNA'sorigin. We studied all tRFs with above-threshold abundances and lengthsbetween 16 and 27 nt inclusive. As we explain in Methods, we imposed alength limit at 27 nt: as a result, 5′-tRHs and 3′-tRHs are not includedin this analysis. To avoid contributions from tRFs of ambiguous origin,which may arise from different biogenesis processes, we focused on onlythe 3,136 tRFs that are exclusive to tRNA space (see Methods). In theDiscussion, we discuss our findings in the context of knownmodifications across the span of mature tRNAs.

We tracked multiple tRF attributes and did so separately for each of the32 cancers (see Methods). We show a holistic view of the results in FIG.3 —for the complete set of the histograms for all of the attributes seeFIG. 14 . Note that we use a white circle to indicate the position ofknown modifications (m1G9, m3C32, m1G37, and m1A58) in the shown tRNAbackbones (23). We stress that we highlight these positions forreference purposes only. Indeed, it is unknown currently whether thesemodifications occur in the tissues and tissue states that arerepresented by the TCGA samples.

In FIG. 3A we see that the more abundant 5′-tRFs have only moderatepreference for the location of their 3′ termini, which span virtuallyall positions from the middle of the D-loop through the beginning of theanticodon loop. Analogously, for the 3′-tRFs, which can terminate at anyof the three nucleotides of the non-templated “CCA” addition, their 5′termini begin just before or within the T-loop. We note that theobserved preferences across human cancers for the 3′ termini of 5′-tRFs,and for the 5′ termini of 3′-tRFs respectively, match the preferencesthat were recently reported for tRFs in the plant A. thaliana (24).

Among the various fragment categories, i-tRFs are the most diverse inboth their 5′ and 3′ termini choices, as we reported recently 4. Intheory, i-tRFs can begin and end at every nucleotide of the mature tRNA,except for its 5′ end or the CCA tail. However, our detailed analysisrevealed that i-tRFs exhibit distinct cleavage patterns in individualcancers. As seen in FIG. 3A, i-tRFs start either close to the 5′ end ofthe tRNA, at the D or the most 5′ half of the anticodon loop or betweenthe variable and the T loop. The 3′ ends of the i-tRFs also favorspecific positions.

We highlight the i-tRF endpoint preferences by examining in more detailthe i-tRFs in LUAD and OV (FIG. 3B). In LUAD, comparatively more i-tRFsbegin inside the yellow region (D-loop) than do in OV. On the otherhand, more i-tRFs begin in the brown region (region C) in OV than do inLUAD. Analogous comments can be made about the i-tRFs' ending positionsin LUAD and OV. See also FIG. 14 for more details.

These findings indicate that the manner in which tRFs are cleaved fromthe respective tRNA depends on the cancer type, on the isodecoder, andon the structural type of the tRF. The findings also argue stronglyagainst the tRFs being random products of tRNA degradation.

Uridylated his(−1) tRFs are Abundant in Human Tissues and Exhibit aUnique Property that is not Affected by Tissue or Tissue State

In eukaryotes, before the mature tRNA^(HisGTG) can be recognized by itscognate aminoacyl tRNA synthetase, guanylation of its 5′-terminus by theenzyme THG1 (THG1L in human) is required (25-27). Thispost-transcriptionally added nucleotide is referred to as the “−1”position and denoted “His(−1).” In recent work with the human breastcancer model cell line BT-474, it was shown that full-length maturetRNAs and 5′-tRHs from tRNA^(HisGTG) also contain a uracil at theHis(−1) position (28). To the best of our knowledge, this possibilityhas not been examined before in human tissues. We therefore sought toprofile the His 5′-tRFs and the identity of their −1 nucleotide acrossall 32 TCGA cancer types.

Our analyses reveal that, in human tissues and across all 32 cancertypes, the largest portion of 5′-tRFs from tRNA^(HisGTG) contains auracil at the His(−1) position—we will refer to them as “−1U 5′-tRFs.” Asmaller fraction of 5′-tRFs contain an adenine at the His(−1) position,whereas 5′-tRFs with a guanine or cytosine are even fewer. The −1U5′-tRFs are exclusive to tRNA space and thus can only be produced byisodecoders of tRNA^(HisGTG). However, it cannot be stated withcertainty whether these −1U 5′-tRFs arise from cleavage of the precursoror from post-transcriptional modification of the mature tRNA: four ofthe 12 isodecoders (the one from MT and the three nucleartRNA-His-GTG-1-6, tRNA-His-GTG-3-1, tRNA-His-GTG-1-5) contain a T atthat location of the DNA template.

Even though the biogenesis of these −1U 5′-tRFs remains elusive, wefound their presence in the numerous TCGA RNA-seq datasets and in a cellline (28) intriguing and set out to study their profiles. Examination of−1U 5′-tRFs from tRNA^(HisGTG) across all 32 TCGA cancer types uncovereda striking property for those −1U 5′-tRFs that differ by a singlenucleotide in their 3′ termini and have lengths between 16 and 24 ntinclusive. In particular, we discovered that as the length of these −1U5′-tRFs increases, their abundance alternates from low to high to low tohigh, etc. Specifically, we discovered that the ratio of abundances ofthese increasingly longer fragments remains constant in all 32 TCGAcancers. Curiously, the pattern of relative abundances was the same forboth the normal and the cancer state. Moreover, we found that thepattern is not exhibited by unmodified 5′-tRFs, i.e. by 5′-tRFs thatbegin at position +1 of the mature tRNA^(HisGTG), to which we refer as+1G 5′-tRFs. FIG. 4 shows the log₂ of the mean ratio of (abundance of−1U 5′-tRF ending at position i)/(abundance of −1U 5′-tRF ending atposition i+1), for BLCA, ESCA, PAAD, BRCA, LUAD, and SKCM. If normalsamples are available, we report values for both the tumor (black) andnormal (light gray) samples. The shown curves are shifted slightly alongthe X-axis with respect to one another in order to make the details ofboth curves visible simultaneously.

This finding suggests that the biogenesis of uridylated His(−1) 5′-tRFsis under exquisite control and that the specifics of this process areconserved in both health and disease, and across tissues. This conservedrelationship suggests that these −1U 5′-tRFs, whether instigators oreffectors, participate in cellular process that are common to all cancertypes, and, thus, of essential nature. The complete collection of theseplots for all 32 cancers can be found in FIG. 15 .

System-Level Networks: tRFs are Positively- and Negatively-Correlatedwith One Another in a Selective Manner

As part of the above analyses, we compiled the profiles of tRFs for all32 cancer types. In our previous work, we found that tRFs from the sameanticodon can be clustered in groups that are explained by the positionwith respect to the mature tRNA and by their lengths (see FIG. 3 ofTelonis et al (4)). Here, we expand the analysis to systematically studythe correlation patterns among tRFs. For each cancer type, we computedpair-wise correlations (Spearman) between tRFs. We only kept tRF-tRFpairs whose correlation value was ≥0.333 or ≤−0.333 and the associatedfalse discovery rate (FDR) was ≤0.01. Multiple tRFs satisfied thesecriteria in each of the 32 cancer types.

Analysis of the resulting correlations revealed that the correlated tRFsexhibit notable properties that pertain to the organelle in which thetRFs are produced, the source isoacceptor, the length of the tRF, andthe structural type of the tRF (Supp. Table S3). Specifically, we foundthe following:

-   -   the expressed tRFs remains essentially the same across the 32        analyzed cancer types (FIG. 16A);    -   the expressed tRFs that participate in tRF-tRF pairs are        characteristically cancer-specific (FIG. 16B);    -   tRFs that are positively correlated with one another originate        almost exclusively in the same cellular compartment (either both        pair members are nuclear tRFs, or, both are MT tRFs);    -   tRFs that are negatively correlated with one another originate        in different compartments (i.e., one of the tRFs comes from the        nucleus and the other from the MT);    -   positively-correlated nuclear tRFs frequently arise from        distinct isoacceptors;    -   positively-correlated MT tRFs frequently arise from the same        isoacceptor;    -   negatively-correlated tRFs frequently arise from distinct        isoacceptors, irrespective of whether they originate in the        nucleus or the MT;    -   positively-correlated tRFs frequently have similar lengths        (length difference <5 nt) and belong to the same structural        category;    -   negatively-correlated tRFs frequently have different lengths        (length difference ≥5 nt) and belong to different structural        categories.        A representative list of tRF:tRF pairs (both positively- and        negatively-correlated), and their corresponding correlation        values and statistical significance, can be found in the Supp.        Table S4 for the case of BRCA.

These results indicate that tRFs are a considerably heterogeneous groupof molecules. The characteristics of tRFs such as isoacceptor of origin,organelle of origin, length and structural type are importantdeterminants of the types of correlations in which they participate. Westress that, despite these commonalities, the choice of which expressedtRFs participate in positively- or negatively-correlated pairs dependson cancer-type.

System-Level Networks: tRFs are Positively- and Negatively-Correlatedwith mRNAs and Pathways in a Selective Manner

From a functional standpoint, others (10) and we (4) have shown thattRFs can be loaded on Argonaute, just like miRNAs. In fact, such loadingwas demonstrated to affect the abundance levels of mRNAs (29). Tocompare and contrast the potential impact of miRNAs and tRFs in thecancer context, we leveraged the available long RNA-seq data of the TCGArepository. As a positive control case, we included miRNAs in theseanalyses. Specifically, we computed all correlated tRF-mRNA andmiRNA-mRNA pairs, and examined their properties across and within cancertypes. These analyses were carried out with the understanding that, forboth miRNAs and tRFs, these anti-correlations capture both directinteractions and indirect relationships. A representative list oftRF:mRNA pairs (both positively- and negatively-correlated), and theircorresponding correlation values and statistical significance, can befound in the Supp. Table S4 for the case of BRCA.

First, we examined whether tRF-mRNA and miRNA-mRNA anti-correlationspersist across cancer types. As we computed abundance correlations, wewere strict when filtering tRFs to minimize the inclusion of noise inour data. We found that the expressed tRFs, miRNAs, and mRNAs areessentially the same across cancers (Supp. Figures S5A, S6A, and S6B).However, what changes dramatically from one cancer type to the next isthe specific manner in which miRNAs and tRFs “partner” with mRNAs toform negatively-correlated pairs. This point is evidenced by the verylow off-diagonal support in Supp. Figures S5B, S6C and S6D. Within acancer, tRFs and miRNAs are frequently negatively correlated with thesame mRNAs, as evidenced by the 2×2 mini-matrices across the diagonal inFIG. 17E. This suggests possible synergistic activities by miRNAs andtRFs.

Next, we examined whether the cancer-specificity of thenegatively-correlated tRF-mRNA and miRNA-mRNA pairs translate intodifferences in the underlying pathways. To investigate this possibility,we performed DAVID analysis for each collection of mRNAs in tRF-mRNApairs in search of enriched Gene Ontology (GO) terms and also KEGGpathways. We observed that the distribution of GO Biological Process(BP) terms as well as of KEGG pathways resembles a power-lawdistribution with many pathways found uniquely in one cancer-type andrelatively fewer pathways appearing in several types (FIGS. 18A-B). Forexample, “renal cell carcinoma” was found enriched among the mRNAs thatare negatively correlated with tRFs in KIRC. However, other enrichedKEGG pathways, such as “pathways in cancer” and “proteoglycans incancer,” are universal. Overall, we observed that any two cancers have asmaller overlap in terms of enriched mRNAs (FIG. 18C) compared toenriched pathways (FIG. 18D). This suggests that, although the tRF-mRNAor miRNA-mRNA correlations are cancer-type-specific, the processes thatare negatively correlated with tRFs and miRNAs are more general.

We then focused on the GO terms for Biological Processes (BP) that wefound to be common to multiple cancer types (Supp. FIGS. S7A and S7B),grouped them into non-redundant clusters (FIG. 18D), and identified fourmain pathways: (a) Transcription, (b) Development and morphogenesis(abbreviated as “Development”), (c) Chromatin organization, and, (d)Cell adhesion and extracellular matrix organization (abbreviated as“Cell adhesion”). Notably, mRNAs from the “Transcription” and “Chromatinorganization” pathways were negatively correlated predominantly withtRFs and exhibited these correlations across the vast majority of cancertypes. On the other hand, mRNAs from the other two pathways(“Development” and “Cell adhesion”) were negatively correlated withmiRNAs, with tRFs or both (FIG. 18D).

Having established the conserved relationship between these fourpathways and the associated tRFs, we examined how often tRFs overlappingisodecoders of a specific isoacceptor are associated with mRNAs from therespective GO term. FIG. 5A shows in heatmap form the fraction of cancertypes in which tRFs overlapping a shown isoacceptor are negativelycorrelated with mRNAs belonging to each pathway.

We see that most tRNA isoacceptors are linked with the same GO terms inmany cancer types. The frequency of those correlations does not dependon the tRNA's genome of origin (mitochondrial vs. nuclear) or theencoded amino acid. We also observe that tRFs from several mitochondrialand nuclear isoacceptors are very often negatively correlated withalmost all examined GO terms. The mitochondrial ValTAC, LeuTAA andProTGG isoacceptors are negatively correlated with mRNAs from all shownGO categories (FIG. 5A) in nearly all cancer types: this is true evenfor pathways whose mRNAs do not have a previously reported mitochondriallink, e.g. “cell adhesion.”

Collectively, the above results provide further support to the view thatthe tRF-mRNA anti-correlations are an integral component of themolecular physiology of cancer, and not random. In fact, the analysisshows that tRF-mRNA anti-correlations parallel miRNA-mRNAanti-correlations (FIG. 5A). It is important to note that the tRF-mRNAanti-correlations comprise tRFs from both the nucleus and themitochondrion, which in turn indicates that the nuclear and MT genomesmarshal the corresponding pathways in a cooperative manner. We willreturn to this point below and discuss five specific examples after ourpresentation of MINTbase.

System-Level Networks: tRFs are Linked to the Cellular Destinations ofProteins Encoded by Negatively-Correlated mRNAs

Spurred by the numerous statistically significant links between tRFs andmiRNAs and pathways, we sought to examine one more facet of theseassociations, namely the cellular localization of the protein productsof the corresponding mRNAs. For this analysis, we treated nuclear tRFsseparately from mitochondrial tRFs. We used information from the UniProtdatabase to distinguish among seven destinations: nucleus, cytoplasm,endoplasmic reticulum (ER) or Golgi, mitochondrion, cell membrane,secreted, and “other” organelles (e.g. vesicles, endosomes, etc.). Also,we distinguished between nuclear tRFs and MT tRFs. For comparisonpurposes, we repeated the cellular destination analysis for the mRNAs inisomiR-mRNA correlations. Note that the protein localization analysis iscarried out at the level of isomiRs for more granularity. For both tRFsand isomiRs, we analyzed positive and negative correlations separately.

The top row of FIG. 5B shows the results for isomiRs. As can be seen,there are many isomiR-mRNA correlations, as shown by the size of thesquares. There is a bimodal behavior with regard to the possible proteindestinations and is independent of the correlation sign. In particular,the isomiR-mRNA correlations are enriched (black-filled boxes) in mRNAswhose protein products are destined for the ‘cell-membrane’ or are‘secreted.’ On the other hand, the correlations are depleted(gray-filled boxes) in mRNAs whose protein products are destined for thenucleus. Note that for negatively-correlated mRNAs, the enrichment ordepletion of the nucleus among possible destinations exhibits morevariability. DLBC, kidney chromophobe (KICH), and KIRP stand out due totheir more complicated patterns.

We repeated the same analysis for nuclearly-encoded (FIG. 5B, middlerow) and MT-encoded tRFs (FIG. 5B, bottom row), respectively. Thedependence on cancer type is more pronounced for tRFs than is forisomiRs. Additionally, the cellular destination pattern is not bimodalanymore. In fact, the proteins produced from mRNAs in tRF-mRNAcorrelations can reach different combination of the considereddestinations in different cancer types. The MT tRFs exhibit a consistentand interesting behavior in almost all cancer types. Specifically, thepositive (but not the negative) MT tRF-mRNA correlations, are enrichedin mRNAs whose protein products are destined for the MT. On the otherhand, the negative (but not the positive) MT tRF-mRNA correlations, areenriched in mRNAs whose protein products either localize to the cellmembrane or are secreted. It is worth briefly discussing the resultspertaining to the two melanomas, uveal melanoma (UVM) and skin cutaneousmelanoma (SKCM). In UVM, nuclear and cytoplasmic proteins are depletedin the set of mRNAs that are positively correlated with nuclear tRFs.However, the mRNAs encoding proteins localizing to these compartmentsare higher in number and/or enriched in the negative correlations withtRFs. This opposite behavior is also evident for the rest of thecompartments in UVM. SKCM provides an example wherein the nuclear and MTtRFs behave differently. In this cancer type, the correlations of MTtRFs with mRNAs are enriched in mRNAs producing proteins that aredestined for the cell membrane or are secreted. On the other hand, thecorrelations of nuclear tRFs with mRNAs are enriched in mRNAs producingproteins that localize to the nucleus.

These results provide strong support to the view that the observedtRF-mRNA pairs are not accidental. In fact, it is reasonable to positpossible synergistic activities between isomiRs and tRFs. Equallyimportantly, the derived associations suggest that information istransferred across compartments in a manner that is not currentlyunderstood.

System-Level Networks: The Genomic Span of mRNAs that are Positively- orNegatively-Correlated with tRFs are Selectively Enriched/Depleted inSpecific Repeat Elements

In light of our earlier work (30-32) and the more recent findings inmouse that connect fragments from tRNA^(GlyGCC) with the MERVL repeatand mRNAs (15), we hypothesized that a link between tRFs and repeatelements exists in human cancers. Separately for each cancer type, wesought to determine whether the corresponding genomic sequences areenriched or depleted in repeat elements, when the orientation of therepeat is sense to the gene or antisense to it. We examined threecollections of sequences: the full (unspliced) genomic span of thecorresponding mRNAs; the union of their introns; and, the union of theirexons. More specifically, for each of the three collections ofsequences, separately for sense and antisense, and for each RepeatMasker(33) category, we determined what fraction of the sequence collection athand corresponds to embedded instances of the repeat family beingconsidered. For each calculated fraction, we examined whether it issignificantly enriched or depleted compared to chance (see Methods).

Panel A of FIG. 5C shows a heatmap of the generated z-scores, for all 32cancer types, for sense and antisense instances of all repeatcategories, and, separately for mRNAs that are positively correlated ornegatively correlated with tRFs. The very high or very low z-scoresstrongly argue that these findings are not random. For clarity, onlyz-scores ≤−2 or ≥+2 are rendered as light gray and black respectively.As panel A of FIG. 5C makes apparent, in all 32 cancer types, thegenomic spans of mRNAs that are either positively or negativelycorrelated with tRFs exhibit significant enrichment or depletion inrepeat elements or their reverse complements.

Our previous studies showed that the distribution of repeats in intronsand exons is not random and that repeats in intronic regions appear toreflect functional conservation in the absence of sequence conservation.With that in mind, we repeated the above calculations by separating eachunspliced mRNA into its intronic and exonic components shown in panels Band C respectively of FIG. 5C. Interestingly, we find that the intronsalone effectively reproduce the above results. Thus, the intronicsequences are responsible for the majority of the observed signal. Whenwe consider only the exonic regions, a lot of the signal effectivelydisappears (panel C of FIG. 5C).

We also inverted our vantage point. For each RepeatMasker family inturn, we counted the number of cancers in which it was enriched. We didthis for the unspliced mRNAs, the intronic regions, and the exonicregions. We find that different combinations of RepeatMasker familiesare enriched in different cancers: the enriched families include DNAtransposons, long interspersed elements (LINE), short interspersedelements (SINE), long terminal repeats (LTR), and other. As can be seenfrom panels A and B (right column) of FIG. 5C, DNA transposons, LINEs,and the ERVL/ERV1 sub-categories of LTRs are prevalent exclusively inthe introns of nearly all of the studied cancers. On the other hand, theALU sub-category of SINEs, is enriched (in sense) in the exons of onehalf of the studied cancer types. Considering that many tRFs haverepeated genomic instances, it is possible that the correlations weobserve are the result of ambiguous tRFs whose multiple genomicinstances outside of tRNA space overlap with mRNAs. We examined allpossible genomic origins of such tRFs and could not find support forthis hypothesis (Methods and FIG. 20 ).

These results provide additional independent support to our earlierfindings that the distribution of repeating sequences in the humangenome is not arbitrary (30-32,34). Moreover, the uncovered associationsbetween tRFs and repeat elements strongly implicate the latter in thelayer of tRF-mediated regulation of expression in nearly all 32 cancertypes.

System-Level Networks: Intra-Cancer Networks of tRFs can be Modulated bya Patient's Sex or a Patient's Race

We hypothesized that tRF profiles differ across sex or race boundariesand investigated the matter in two cancer types for which sex-dependentand race-dependent disparities of genetic origin, respectively, havebeen documented in the literature. Spurring this hypothesis is the abovefinding that tRFs are strongly associated with tRFs, mRNAs, and proteinsthat localize to specific cellular compartments.

In the below analysis, we limit ourselves to three of the 32 cancerstypes contained in TCGA: lung adenocarcinoma (LUAD), the subtype ofbreast cancer (BRCA) known as “triple negative” (TNBC), and bladdercancer (BLCA). Additional in-depth studies that escape the scope of thispresentation will be necessary in order to examine whether the analysisof RNA-seq datasets from other TCGA cancer types supports similarfindings. We stress here that mining RNA-seq data is distinctly unlikethe task of detecting, e.g., race-based somatic mutations, for whichTCGA is well known to be under-powered (35). LUAD and TNBC are examinedimmediately below from the standpoint of correlations among tRFs. BLCAis examined later in the presentation from the standpoint ofsex-dependent tRF-mRNA correlations abundances.

The first of the three cancer types is LUAD. In lung cancer, both sexand race disparities are known to exist. A portion of these disparitiescan be attributed to differences in the stage and degree of adoption oftobacco smoking (36-41). However, age-adjusted lung cancer incidencerate is higher among black men compared to white. Also, it is roughlyequal between black and white women, even though black men and blackwomen have a lower overall exposure to cigarette smoke. Theseobservations suggest that sex and race contribute to these differences(42). Below, we examine only the sex-dependence aspect of LUAD.

The second cancer type that we examine is TNBC. TNBC representsapproximately 15-20% of the BRCA cases (43) and is the most aggressiveBRCA subtype, characterized by poor prognosis. In the absence of anexpressed hormone receptor, chemotherapy continues to remain the onlysystemic option for TNBC patients (44). TNBC is twice as frequent amongB/Aa premenopausal women compared to Wh women (44-49). In what follows,we examine the race-dependence aspect of TNBC.

In each case, we formed networks of tRFs whose expression values werestatistically significantly correlated: we only kept relationships witha Spearman correlation ≥0.33 or ≤−0.33 and a matching false discoveryrate (FDR)≤0.05. Then, we examined whether and how these networkschanged between males and females in LUAD and between White andBlack/African American patients with TNBC.

Case: LUAD. We analyzed the lung adenocarcinoma samples from TCGAseparately for male and for female patients. FIG. 6A shows the networkof negatively correlated tRF pairs that satisfy the correlation valueand FDR thresholds mentioned above and are supported by the LUAD samplesin TCGA that belong to either male or female patients. The networks arefurther encoded based on source isoacceptor, structural category of tRF,tRF length, and the tRFs' genome of origin. FIG. 6B shows the subset ofedges and vertices that correspond to tRF-tRF correlations that areexclusive to male LUAD patients. FIG. 6C shows the subset of edges andvertices that correspond to tRF-tRF correlations that are exclusive tofemale LUAD patients. FIG. 6D shows the subset of edges and verticesthat correspond to tRF-tRF correlations that are present in both maleand female LUAD patients. As can be seen, female LUAD patients exhibitmore and more-widespread anti-correlations compared to male patients.

Case: TNBC. We analyzed the TNBC samples from TCGA and created analogousnetworks. Here, it is the networks of positively-correlated tRF pairsthat show characteristic differences between White (Wh) andBlack/African American (B/Aa) patients with TNBC (see“Nomenclature/Notation” in Methods). FIG. 19 shows the network oftRF-tRF pairs for all TNBC patients, the subset of the network that ispresent only in Wh TNBC patients, only in B/Aa TNBC patients, and, inboth Wh and B/Aa patients. As in the case of LUAD, there are evidentdifferences in the networks of correlations that are present in the Whand B/Aa TNBC patients, respectively.

The Discovered TCGA tRFs can be Studied Using a Newly-Added MINTbaseModule

We recently reported the development of MINTbase, a framework forstoring and studying tRNA fragments 22. MINTbase is both a web-basedcontent repository and a tool for the interactive study of tRFs.Originally, we populated MINTbase with 7,129 unique and statisticallysignificant tRFs that resulted from our analyses of 832 public datasets(4,8,9,22).

We have now extended MINTbase (version 2.0) to include the tRFs that wegenerated in our analyses of TCGA. With the addition of the tRFs from 32TCGA cancer types, MINTbase now comprises information about thelocation, normalized abundances, and expression patterns of 26,531distinct tRFs compiled by mining a total of 11,719 public datasets fromTCGA and elsewhere.

To extend the utility of the repository, we augmented its searchcapabilities. Specifically, we now allow the user to search using a TCGAcancer abbreviation (e.g. BRCA, PRAD, PAAD, etc.), a descriptive phrase(e.g. breast cancer), one or more structural categories, one or moreisoacceptors, a sequence (e.g. GGCTCCGTGGCGCAATGGA), a tRNA name, or atRNA label, and to combine these choices with a “minimum abundance”criterion. As an example, the following complex Boolean request can beexecuted by pointing-and-clicking: “retrieve all 5′-tRFs and all i-tRFsthat overlap with either the mitochondrial isodecoder of tRNA^(AspGTC)or any of the nuclear isodecoders of tRNA^(HisGTG) and are present inany of the breast cancer samples of MINTbase with abundance ≥25 RPM.”

Each of MINTbase's 26,531 tRFs has its own exclusive record that listsall publicly known identifiers for it, information about theisodecoder(s) that contain it, a multiple sequence alignment in the caseof multiple tRNA origins, whether the tRF is exclusive to tRNA space(4,8,9), and how many of the MINTbase datasets contain the tRF with anabundance of ≥1.0 RPM.

To enable intra-TCGA comparisons as well as comparisons between TCGA andnon-TCGA datasets, each tRF record includes four histograms that show:the fraction of datasets containing the tRF in each TCGA cancer type andoutside TCGA; the tRF's distribution of abundances in each TCGA cancertype and in non-TCGA datasets; and, two more histograms showingbox-plots of the distribution of abundances of the tRF within each TCGAdataset using a linear and a log₂ Y-axis, respectively. All fourhistograms are interactive and allow the user to select which dataset(s)to display. In FIGS. 7A-7C, we show three of the four histograms fromthe record of the −1U 5′-tRF from tRNA^(HisGTG) with sequenceTGCCGTGATCGTATAGTGGTT. FIG. 7A shows that the tRF is present in at least75% of the samples that are available for 31 of the 32 TCGA cancertypes. The only exception is LAML where the fragment appears in only 29of the 191 datasets. Of the 521 non-TCGA datasets currently contained inMINTbase, the fragment is present in only 8 of them. Across the TCGAdatasets in which it is present, this −1U 5′-tRF exhibits a wide rangeof abundances that reach as high as 1,394.78 RPM in LIHC (not shown). Todemonstrate the comparative differences of the fragment's distributionof abundances, we selected and show the histogram bars for PRAD, COAD,LUSC, UCEC, SKCM, PAAD, and UVM (FIG. 7B). For the same set of cancertypes, in FIG. 7C, we also show the box-plot of their abundancedistributions (note that this panel uses a log₂ Y-axis!). To facilitateinclusion in user reports, all these diagrams can be saved in PNG, JPG,PDF or SVG format.

tRF Correlations with mRNAs from Specific Pathways

Above, we presented evidence of tRF correlations with mRNAs fromspecific pathways. Despite differences in the actual tRFs and mRNAs thatform the correlations, a considerable number of pathways whose membersare correlated with tRFs are common across the 32 cancer types. Wehighlight this with the help of two specific examples (FIG. 9A-9B). Onesuch pathway, Oxidative Phosphorylation, is found enriched in thetRF-mRNA correlations in 22 of the 32 cancer types. In FIG. 9A, wevisualize correlations of tRFs with mRNAs that encode for components ofthe four complexes of mitochondrial respiration and occur in at leastthree cancer types. A second pathway involves mRNAs that are linked tothe “Ribosome.” In FIG. 9B, we visualize correlations of tRFs with mRNAsthat correspond to the “small subunit” ribosomal proteins, the “largesubunit” ribosomal proteins, or to “mitochondrial” ribosomal proteins.Again, these correlations occur in at least three cancer types.

tRF Correlations with mRNAs that are Cancer-Specific

Having shown two examples of tRF-mRNA similarities across cancers, wefocus next on an example of tRF correlations with mRNAs that arecharacteristically cancer-specific. To this end, the integrin family ofproteins serves as a good platform. To help highlight the extent of thecancer-dependencies we show differences between in tRF-mRNA andisomiR-mRNA correlations using three different cancers: OV (FIG. 10A),LGG (FIG. 10B) and LAML (FIG. 10C). Note that in all three of theseFigures, we show the same underlying network of protein-proteininteractions involving integrins, and proteins that interact withintegrins. It is important to stress that for clarity purposes, thenodes labeled with the names of miRNA loci are meant to represent one ormore isomiRs from the corresponding locus that are correlated with thecorresponding mRNAs. Analogously, the nodes labeled with the names oftRNA isoacceptors are meant to represent one or more tRFs from the tRNAtemplate that are correlated with the corresponding mRNAs. As can beseen from FIG. 10A, in OV, isomiRs are involved in comparatively morecorrelations with the shown mRNAs than tRFs. In LGG, none of the showncorrelations involve tRFs. Lastly, in LAML, the correlations with theshown mRNAs involve primarily tRFs. Note also how many more of thefamily's mRNAs are correlated with tRFs and isomiRs in OV, compared toLGG.

Sex Disparities in BLCA

BLCA is four times more likely to develop in men, yet women present withmore advanced disease and have worse survival rates (82-86). Thisdisparity is believed to be due to a differential exposure tocarcinogens as well as the result of genetic, anatomical, and hormonalfactors. BLCA is also known to depend on a patient's race, withincidence rates in White patients double those in with Black/AfricanAmericans, although patients in the latter group have a higher mortalityrate (87-91). We are not aware of previous work that examined thepossibility of molecular links between these disparities and tRFs.

We correlated tRFs with mRNAs in the BLCA tumor samples from Whitepatients, and did so separately for each sex. A first, rather strikingobservation pertains to the number of correlations per tRNA isoacceptor,as a function of the patients' sex. As can be seen in FIG. 11A, the sameisoacceptor can be associated with markedly different numbers of mRNAsin male (X-axis) and female (Y-axis) BLCA patients. The further awayfrom the diagonal a point is, the higher the disparity in the number ofcorrelated mRNAs. Note how many more isoacceptors are correlated withmRNAs that are more numerous in women than in men (points sit above thediagonal). Isoacceptors for which the difference in the ratio is ≥2× (or≤½×) are indicated with black-filled or white-filled circles and arelabeled. It is evident that some of the tRFs, e.g. those arising fromthe nuclear tRNA^(MetCAT) (respectively, the nuclear tRNA^(HisGTG))participate in characteristically more correlations with mRNAs in men(respectively, in women).

We also analyzed tRF-mRNA correlations in BLCA and distinguished betweencorrelations that are present in both male and female patients, andcorrelations that are present in only one of the two patient groups(only female or only male). One group of mRNAs highlights thesesex-based differences: cyclin-dependent kinases (CDK), or proteinsinteracting with CDKs, exhibit pronounced differences in theircorrelations with tRFs based on the sex of the patient. This issummarized in FIG. 11B.

In our discussion above of FIG. 8 , we emphasized that, because of theuncovered cancer dependencies, it is important to know the cancer typein order to establish the context in which a tRF could interact with amRNA, and also determine whether a tRF and an mRNA are positively- ornegatively-correlated. As already mentioned, the therapeuticintervention would aim to counterbalance the mRNA deregulation. For agiven tRF:mRNA correlation, this counterbalancing could be achieved bymodulating the abundance of the tRF, by modulating the abundance of themRNA, or by modulating the abundance of one or more tRFs or of one ormore mRNAs simultaneously.

With that in mind, and as a case in point, we focused on thenegatively-correlated tRF-mRNA pairs among those that we generated fromour analysis of the TCGA datasets. Note that even though we focus onnegatively-correlated pairs, the analysis extends trivially topositively-correlated pairs and the methodology is the same.

First, among the negatively-correlated tRF-mRNA pairs that are presentin a given cancer i we identified those tRFs that participate incorrelations with at least 20 different mRNAs. The threshold T, in thiscase “20,” was chosen arbitrarily and can be changed and optimized asneeded. For each tRF that exceeded the threshold T in cancer i, weidentified the number of mRNAs with which the tRF forms negativecorrelations in each of the remaining cancers (in this case, 31 cancers)and computed the average A of negative correlations across these othercancers. Of the tRFs that exceeded threshold T in cancer i wesub-selected and kept only those for which A was not more than a secondthreshold t: in this case, we selected t to be 3—this threshold t canagain be changed and optimized as needed. By selecting a large value forthreshold T and a small value for threshold t we seek to identify thosetRFs that participate in ≥T negative-correlated tRF-mRNA pairs in canceri and ≤t such pairs on average in the remaining cancers underconsideration, i.e. in comparatively fewer pairs in the other cancers.For the thresholds we selected here, the emerging tRFs participate in atleast six times more negative correlations with mRNAs in cancer i thanthe average number of correlations in the remaining cancers. Clearly,the higher the difference between T and t, the more specific the tRFunder consideration is for cancer i.

We carried out this analysis for each of the 32 TCGA cancers in turn,using a value of 20 for T and a value of 3 for t. Thusly, we identified,separately for each TCGA cancer, a number of tRFs that participate incancer-specific negative correlations with mRNAs and could serve astargets of therapeutic intervention. The same methodology could berepeated using different thresholds, or by focusing onpositively-correlated tRF-mRNA pairs, or by focusing on positively-aswell as negatively correlated tRF-mRNA pairs. Accordingly, thesefollowing sequences can be utilized in methods of treatment.

For ACC (Adrenocortical carcinoma), we found that the tRFs withsequences SEQ 1 through SEQ 31 satisfy these criteria.

For BLCA (Bladder Urothelial Carcinoma), we found that the tRFs withsequences SEQ 32 through SEQ 40 satisfy these criteria.

For BRCA (Breast invasive carcinoma), we found that the tRFs withsequences SEQ 41 through SEQ 58 satisfy these criteria.

For CESC (Cervical squamous cell carcinoma and endocervicaladenocarcinoma), we found that the tRFs with sequences SEQ 59 throughSEQ 71 satisfy these criteria.

For COAD (Colon adenocarcinoma), we found that the tRFs with sequencesSEQ 72 through SEQ 77 satisfy these criteria.

For DLBC (Lymphoid Neoplasm Diffuse Large B-cell Lymphoma), we foundthat the tRFs with sequences SEQ 78 through SEQ 88 satisfy thesecriteria.

For ESCA (Esophageal carcinoma), we found that the tRFs with sequencesSEQ 89 through SEQ 94 satisfy these criteria.

For HNSC (Head and Neck squamous cell carcinoma), we found that the tRFswith sequences SEQ 95 through SEQ 109 satisfy these criteria.

For KICH (Kidney Chromophobe), we found that the tRFs with sequences SEQ110 through SEQ 120 satisfy these criteria.

For KIRC (Kidney renal clear cell carcinoma), we found that the tRFswith sequences SEQ 121 through SEQ 128 satisfy these criteria.

For KIRP (Kidney renal papillary cell carcinoma), we found that the tRFswith sequences SEQ 129 through SEQ 132 satisfy these criteria.

For LAML (Acute Myeloid Leukemia), we found that the tRFs with sequencesSEQ 133 through SEQ 165 satisfy these criteria.

For LGG (Brain Lower Grade Glioma), we found that the tRFs withsequences SEQ 166 through SEQ 180 satisfy these criteria.

For LIHC (Liver hepatocellular carcinoma), we found that the tRFs withsequences SEQ 181 through SEQ 186 satisfy these criteria.

For LUAD (Lung adenocarcinoma), we found that the tRFs with sequencesSEQ 187 through SEQ 194 satisfy these criteria.

For LUSC (Lung squamous cell carcinoma), we found that the tRFs withsequences SEQ 195 through SEQ 217 satisfy these criteria.

For MESO (Mesothelioma), we found that the tRFs with sequences SEQ 218through SEQ 235 satisfy these criteria.

For OV (Ovarian serous cystadenocarcinoma), we found that the tRF withsequence SEQ 236 satisfies these criteria.

For PAAD (Pancreatic adenocarcinoma), we found that the tRFs withsequences SEQ 237 through SEQ 242 satisfy these criteria.

For PCPG (Pheochromocytoma and Paraganglioma), we found that the tRFswith sequences SEQ 243 through SEQ 251 satisfy these criteria.

For PRAD (Prostate adenocarcinoma), we found that the tRFs withsequences SEQ 252 through SEQ 261 satisfy these criteria.

For READ (Rectum adenocarcinoma), we found that the tRFs with sequencesSEQ 262 through SEQ 270 satisfy these criteria.

For SARC (Sarcoma), we found that the tRFs with sequences SEQ 271through SEQ 275 satisfy these criteria.

For SKCM (Skin Cutaneous Melanoma), we found that the tRFs withsequences SEQ 276 through SEQ 283 satisfy these criteria.

For STAD (Stomach adenocarcinoma), we found that the tRFs with sequencesSEQ 284 through SEQ 291 satisfy these criteria.

For TGCT (Testicular Germ Cell Tumors), we found that the tRFs withsequences SEQ 292 through SEQ 308 satisfy these criteria.

For THCA (Thyroid carcinoma), we found that the tRFs with sequences SEQ309 through SEQ 324 satisfy these criteria.

For THYM (Thymoma), we found that the tRFs with sequences SEQ 325through SEQ 331 satisfy these criteria.

For UCEC (Uterine Corpus Endometrial Carcinoma), we found that the tRFswith sequences SEQ 332 through SEQ 344 satisfy these criteria.

For UVM (Uveal Melanoma), we found that the tRFs with sequences SEQ 345through SEQ 372 satisfy these criteria.

In each of these cancer types, a possible therapeutic intervention wouldcomprise modulating the abundance of one or more of the correspondingtRFs. The modulation would comprise increasing the abundance of one ormore of these tRFs, decrease the abundance of one or more of these tRFs,or a combination.

Second, we also reversed our vantage point as follows. Among thenegatively-correlated tRF-mRNA pairs that are present in a given canceri we identified those mRNAs that participated in correlations with atleast 20 different tRFs. The threshold T, in this case “20,” was chosenarbitrarily and can be changed and optimized as needed. For each mRNAthat exceeded the threshold T in cancer i, we identified the number oftRFs with which the mRNA forms negative correlations in each of theremaining cancers (in this case, 31 cancers) and computed the averagenumber A of negative correlations across these other cancers. Of themRNAs that exceeded threshold T in cancer i we sub-selected and keptonly those for which A was not more than a second threshold t: in thiscase, we selected t to be 3—this threshold t can again be changed andoptimized as needed. By selecting a large value for threshold T and asmall value for threshold t we seek to identify those mRNAs thatparticipate in ≥T negative-correlated tRF-mRNA pairs in cancer i and ≤tsuch pairs on average in the remaining cancers under considerations,i.e. in comparatively fewer pairs in the other cancers. For thethresholds we selected here, the emerging mRNAs participate in at leastsix times more negative correlations with tRFs in cancer i than theaverage number of correlations in the remaining cancers. Clearly, thehigher the difference between T and t, the more specific the mRNA underconsideration is for cancer i.

We carried out this analysis for each of the 32 TCGA cancers in turn,using a value of 20 for T and a value of 3 for t. Thusly, we identified,separately for each TCGA cancer, a number of mRNAs that participate incancer-specific negative correlations with tRFs and could serve astargets of therapeutic intervention. The same methodology could berepeated using different thresholds, or by focusing onpositively-correlated tRF-mRNA pairs, or by focusing on positively-aswell as negatively correlated tRF-mRNA pairs.

For ACC (Adrenocortical carcinoma), we found that the mRNAs of thefollowing genes satisfy these criteria: CSDC2, CSGALNACT1, RERG, PCMTD1,PLCB3, YEATS2, BIRC2, MVP, MYST3, ARL6IP5, TRANK1, TMEM45A, ACVR1, PGCP,VCL, MSRA, C10orf54, DCUN1D3, CTDSPL2, SIK2, TMCO6, SRCAP, TMEM159,PLEKHO2, HLA-E, TAX1BP3, C11orf75, RCE1, NDRG4, MR1, MARK2, FAM21B,HLA-B, RBL2, CABC1.

For BLCA (Bladder Urothelial Carcinoma), we found that the mRNAs of thefollowing genes satisfy these criteria: ACTG2, TGFBR3, PRELP, RERE,OSR1, TCEAL1, NNAT, GCOM1, MMP2, MYST4, SYNPO2, C16orf45, FYCO1, MYH11,CSRP1, MEIS2, ACTA2, CLU, LOXL1, IGFBP4, TXNIP, SLIT3, CHRDL2, MYL9.

For BRCA (Breast invasive carcinoma), we found that the mRNAs of thefollowing genes satisfy these criteria: CRTC1, CALCOCO1, SLC27A1, CROCC,PGPEP1, PSD4, TBC1D17, PHF15, ARAP1, TNFRSF14, NISCH, MED16, RGS12,MYO15B, AGXT2L2, RFX1, C21orf2, NEURL4, TPCN1, HOOK2, LTBP3, SPHK2,ABTB1, ABCD4, ZBTB48, CIRBP, CYTH2, ZNF446, PHF1, RPS9, MZF1, FAM160A2,KIF13B, GLTSCR2, WDR81, SH2B1, RHOBTB2, CRY2, LTBP4, HDAC7, ZNF219,MUM1, RBM5, RAPGEF3, CCDC9.

For CESC (Cervical squamous cell carcinoma and endocervicaladenocarcinoma), we found that the mRNAs of the following genes satisfythese criteria: NBPF10, JRK, SMG5, ALDH1A2, MFAP4, ZFYVE1, CDC42BPB,ENTPD4, IGF1, ZFYVE26, APOLD1, LOC200030, KIAA0430, UBN1, VASH1,RANBP10, WDR37, MGP, MON1B, CNN1, MAT2A, PGR, KIAA0100, C14orf21, HCFC1,LRP10, DIDO1, FBXL18, ATP6V0A1, RGS2, MLXIP, TRIM56, CTGF, KIAA0284,DES, SFRP4, PDPK1, TAOK2, SMCR8, CLN8, UNC119B, TRIM25, CYB561D1,TBC1D2B, DNAJC5, CRAMP1L, ZNF646, ZC3HAV1, KHNYN, PSKH1, RGS1.

For COAD (Colon adenocarcinoma), we found that the mRNAs of thefollowing genes satisfy these criteria: ATP8B2, SYNE1, WIPF1, AOC3,LIMS1, FZD1, CYBB, MAFB, GIMAP6, REST, STAB1, FPR3, MSRB3, FRMD6,CALCRL, MPEG1, MYLK, ELTD1, FGL2, SPARCL1, PLXDC2, LAIR1, ITGB2, NRP1,MRC1, ZEB1, SYT11, NCKAP1L, AXL, APLNR, ZEB2, EDIL3, FERMT2, PTPRM,RASSF2, PKD2, PHLDB2, TCF4, IL10RA, HEG1, HIPK3, NEXN, TMEM140, AMOTL1,A2M, TIE1, AKT3, CD163, LPHN2, OSMR, CSF1R, DAAM2, IL1R1, GPC6, SLC8A1,FBN1, GNB4, GPNMB, DOCK2, KIAA1462, CSF2RB, MYO5A, S1PR1, ARHGEF6.

For DLBC (Lymphoid Neoplasm Diffuse Large B-cell Lymphoma), we foundthat the mRNAs of the following genes satisfy these criteria: GOLGA2,DCHS1, CLDND1, CSRNP2, FRMD8, SLCO2A1, ARHGAP23, NID1, DUSP7, TBC1D20,YAP1, WDR82, TMEM43, TJP1, CARD8, ZNF213, KIAA0232, EPAS1, VPS11, PHC1,SKI, DAG1, ANKRD40, FAT1, PHF12.

For ESCA (Esophageal carcinoma), we found that the mRNAs of thefollowing genes satisfy these criteria: SSC5D, PDLIM3, CELF2, TIMP3,ABCC9, CALD1, COL8A1, GREM1, THBS4, PRUNE2, TMEM47, PBXIP1, PLN, CCDC80,C7, PODN, DDR2, PPP1R12B, MRVI1, LMOD1, C7orf58, HSPB7, TAGLN, PPP1R16B,GFRA1, LOC728264, SGCD, PGM5.

For HNSC (Head and Neck squamous cell carcinoma), we found that themRNAs of the following genes satisfy these criteria: GABBR1, C14orf179,METT11D1, C6orf125, ZNF692, FKBP2, FAM113A, LOC388789, TAZ, WASH3P,CDK5RAP3, PLBD2, SDR39U1, CPT1B, UBL5, C14orf2, LOC146880, THAP3,ANKRD13D, C12orf47, ATP5E, ATPIF1, SYF2, C8orf59, WASH7P, NPIPL3, CDK10,C1orf151, MRPS21, C19orf60, C7orf47, CENPT, GAS5, KIFC2, NFIC, RPL39,UQCRB, COX6C, LUC7L, CCS, COMMD6, ZNF133, SNHG12, C11orf31, NPEPL1.

For KICH (Kidney Chromophobe), we found that the mRNAs of the followinggenes satisfy these criteria: BMPR1A, EXT1, TFAM, PDCD11, MTIF2, POLR3A,MAPK8, PRDX3, COQ5.

For KIRC (Kidney renal clear cell carcinoma), we found that the mRNAs ofthe following genes satisfy these criteria: ARHGAP19, KIAA1671,KIAA0754, KIAA1147, ZNF45, KLF13, MYO9A, FUT11, ASH1L, KIF13A, TUBGCP3,MTF1, FAM168A.

For KIRP (Kidney renal papillary cell carcinoma), we found that themRNAs of the following genes satisfy these criteria: GLCCI1, CDK13,POGZ, UBN2, CREBZF, NPHP3, VEZF1, CHD1, YPEL2, LRRC37B2, GPATCH8, ENC1,TTC18, Cllorf61, RSBN1L, EFNB2, PHIP, RBAK, SPEN, RBM9, SMURF2, ZNF264,ZNF587, PTPN12, TPBG, RBM33, DMTF1, CCNT2, ARID4B, ARGLU1, CREB1,KIAA0753, BTAF1, C17orf85, RLF, MLL5, ZFC3H1, ZNF160, PRPF38B, SETD5,ARRDC4, HOOK3, RC3H1, MLL3, RNF207, MAP3K1, PLEKHH2, CCDC57, DAPK1,LUC7L3.

For LAML (Acute Myeloid Leukemia), we found that the mRNAs of thefollowing genes satisfy these criteria: SUPT5H, SKIV2L, IKBKG, HGS,MIB2, MED15, STK25, ANAPC2, RHOT2, SFI1, CUL9, ARHGEF1, GTPBP2,KIAA0892, MBD1, UCKL1, DHX16, ZFYVE27, APBA3, PI4 KB, C19orf6, SPSB3,CAPN10, FLYWCH1, ATG4B, CDC37, LZTR1, MAN2C1, C1orf63, DVL1, EDC4,DHX34, PCNXL3, EXOC3, FUK, FBXL6, LMF2, HDAC10, E4F1, TSC2, ZDHHC8,CPSF3L, FAM160B2, CLCN7, LRRC14, D2HGDH, ZNF335, FHOD1, SOLH, ZBTB17,POLRMT, SLC26A1, KIAA0415, SELO, SAPS2, NME3, KLHL36, SCYL1, USP19,DGKZ, CYHR1, ATG2A, VPS16, XAB2, ACTR5, ZNF76, ATP13A1, RNF31, GPN2,MUS81, FAM73B, TTC15, CXXC1, TRMT2A, WDR8, PTGES2, TELO2, RFNG,SLC39A13.

For LGG (Brain Lower Grade Glioma), we found that the mRNA of thefollowing gene satisfies these criteria: EXD3.

For LIHC (Liver hepatocellular carcinoma), we found that the mRNA of thefollowing gene satisfies these criteria: DYRK2.

For LUAD (Lung adenocarcinoma), we found that the mRNAs of the followinggenes satisfy these criteria: CROCCL1, RHPN1, ABCA7, RGL3, PDXDC2,ENGASE, ATG16L2, CSAD, TTLL3, ARHGEF12, ANKS3, LOC100132287, SGSM2,HEXDC, LPIN3, ACCS, PLEKHMIP, ANO9, ELMOD3, KIAA0895L, AP1G2, ACAP3,ECHDC2, NXF1, JMJD7-PLA2G4B, TMEM175, CCDC64B, ANKMY1.

For LUSC (Lung squamous cell carcinoma), we found that the mRNAs of thefollowing genes satisfy these criteria: PKD1, CHKB-CPT1B, WDR90, MACF1,RBM6, LENG8, TAF1C, COL16A1, CAPN12, RBM39, ACIN1, FNBP4, PILRB, DMPK,SFRS5, AHSA2, RBM25, PLCG1, SNRNP70, NCRNA00201, GIGYF1, SRRM2, GOLGA8B,ZGPAT, RTEL1, COL27A1, MAPK8IP3, PABPC1L, HSPG2, AKAP13, LRP1, NKTR,ATAD3B, TUBGCP6, ZNF276, MICALL2, CLCN6, NSUN5P2, NEAT1, LAMA5, CHD2,PPP1R12C, FAM193B, NPIP, CDK11A, STX16, LTBP2, LOC91316, NBEAL2,FLJ45340, LRDD, CCDC88B, GOLGA8A.

For MESO (Mesothelioma), we found that the mRNAs of the following genessatisfy these criteria: C15orf40, SNUPN, CHTF8, CLNS1A, CSNK1D, DPF2,PCIF1, DNAJC4, SECISBP2, C5orf32, RPRD1B, RPL38, NDUFA10, RPRM, BAT4,RSL1D1.

For OV (Ovarian serous cystadenocarcinoma), we found that the mRNAs ofthe following genes satisfy these criteria: KIAA0907, ULK3.

For PAAD (Pancreatic adenocarcinoma), we found that the mRNAs of thefollowing genes satisfy these criteria: NUAK1, KBTBD4, HMCN1, DSTYK.

For PCPG (Pheochromocytoma and Paraganglioma), we found that the mRNAsof the following genes satisfy these criteria: CACNA2D1, TP53BP1,KIAA1244, MARCH8, PCDHGC4, ESYT2, DHX15, TECPR1, MAP3K2, TBC1D24, PCDH1,IPO11, MGAT5, TRAM2, ADAM10, GNA11, CBX6, SNURF, RIF1, CNTN1, LMBRD2,CAND1, TRIP12, RC3H2, PAK3, TMCO3, CSNK2A1P, ASB1, AKAP2, ROCK2, NUP155,PIK3C3, KLHDC10, RAB35, GTF2I, HSPA8, FAM49A.

For PRAD (Prostate adenocarcinoma), we found that the mRNAs of thefollowing genes satisfy these criteria: FEM1B, TGOLN2, SEPT9, MYOCD,LUZP1, TLN1, PIAS1, RNF111, DCBLD2, URB1, ZBTB40, ZNF516, ATXN1L,RHBDD1, HUWE1, VPS13D, ITPR1, NNT, ERC1.

For READ (Rectum adenocarcinoma), we found that the mRNAs of thefollowing genes satisfy these criteria: FZD4, CD93, DYNC1I2, ENG, ELK3,KDR, FAM101B, PXDN, GIMAP4, F13A1, VCAM1, ARHGAP31, CD34, GNG2, LCP2,VWF, CSF1, GPR116, KIRREL, MMRN2, ETS1, ITGA4, FAM120B.

For SARC (Sarcoma), we found that the mRNAs of the following genessatisfy these criteria: SH3BGRL, SORBS1, MAPK4, LYNX1, MICAL3, AKAP1,LIMS2, RNF38, LOC283174, CAND2, PLIN4, MOAP1, RNF19A, RABGAP1, C5orf4,FRY, ARHGEF17, SETMAR, SSH3, NUMA1, PBX1, TOR1AIP1, TACC2, RAB3D, BBS1,CEP68, GPRASP1, SVIL, CRBN, CRTC3, ZFYVE21, SLMAP, RASL12, SCAPER,STAT5B, ZAK, EZH1.

For SKCM (Skin Cutaneous Melanoma), we found that the mRNAs of thefollowing genes satisfy these criteria: MLL4, LMTK2, APBB3, C17orf56,LOC388796, ANO8.

For STAD (Stomach adenocarcinoma), we found that the mRNAs of thefollowing genes satisfy these criteria: KCNMA1, MAST4, FHL1, ATP2B4,TENC1, C20orf194, ASB2, C10orf26, TTC28, FAM13B, ITPKB, GNAO1, FAM129A,ZBTB4, FNBP1, FLNA, CCDC69, STON1, NFASC, PAPLN, ADCY5, LPP, NEGR1,ABI3BP, INPP5B, TNXB, ANGPTL1, ANK2, EPHA3, ZCCHC24, SETBP1, PRICKLE2,LTBP1, RGMA, DARC, KANK2, SYNPO.

For TGCT (Testicular Germ Cell Tumors), we found that the mRNAs of thefollowing genes satisfy these criteria: ATF6, CTDSP2, MKL2, TEAD1,ZNF407, TRAPPC9, AHDC1, LANCL1, KCTD20, OXR1, SNX1, CSNK1G1, KIAA0247,LDOC1L, EPC1, GRLF1, ABHD2, RAIl, ARID1B, ITFG1, MUT, KIAA1737, LAMA2,KIAA1109, CCNI, DIXDC1, C6orf89, RNF144A, APPBP2, KLF12, ZFP91.

For THCA (Thyroid carcinoma), we found that the mRNAs of the followinggenes satisfy these criteria: KIAA0495, PHF21A, ZBTB5, SFRS6, NCOA5,ZNF814, IP6K2, IFT140, INTS3, ZNF559, SETD4, TGIF2, VILL, KCNC3, UBE2G2,FBXO9, IPW, DUOXA1, CACNA2D2, EFHC1, FAM189A2, GTF2IRD2P1, KIAA1683,AP4B1, SCAND2, CDRT4, UNKL, NYNRIN, ARMC5, MAPKBP1, USP40, VEGFA, OLFM2,FBF1, TCF7L1, MXD4, IKBKB, POFUT2, BOC, TCF7L2, RMST, TRO.

For THYM (Thymoma), we found that the mRNAs of the following genessatisfy these criteria: ZFYVE9, LOC399959, SIX1, PDGFC.

For UCEC (Uterine Corpus Endometrial Carcinoma), we found that the mRNAsof the following genes satisfy these criteria: RNMT, SON, MDN1, CELF1,RIPK1, YLPM1, XRN2, BPTF, RQCD1, PAFAH1B2, BOD1L, SBNO1, RNF169, PIK3R4,LRCH3, DCP1A, SF3A1, SLC9A8, TNRC6A, BRPF3.

For UCS (Uterine Carcinosarcoma), we found that the mRNA of thefollowing gene satisfies these criteria: RHBDF1.

For UVM (Uveal Melanoma), we found that the mRNAs of the following genessatisfy these criteria: MAP1A, TCIRG1, ECM1, C14orf159, WARS, PCYOX1L.

In each of these cancer types, a possible therapeutic intervention wouldcomprise modulating the abundance of one or more of the correspondingmRNAs. The modulation would comprise increasing the abundance of one ormore of these mRNAs, decrease the abundance of one or more of thesemRNAs, or a combination.

Lastly, yet another possible therapeutic intervention would comprisemodulating the abundance of one or more of the above-mentionedcancer-specific tRFs in combination with modulating the abundance of oneor more of the above-mentioned cancer-specific mRNAs.

Discussion

We carried out a comprehensive mining of 11,198 datasets from TCGA insearch of statistically significant tRNA fragments. 10,274 of thesedatasets representing 32 human cancer types had associated records thatare devoid of any special annotations (“whitelisted”) and entered ourdownstream analyses. We found that nearly all tRNAs exhibitcancer-specific cleavage patterns. Additionally, we found thatnucleus-encoded and MT-encoded tRNAs exhibit distinctly differentbehavior vis-à-vis to patterning and the abundance of the tRFs theygenerate. tRNA^(HisGTG) represents an exception in that it gives rise toa specific collection of 5′-tRFs that contain a uracil in their −1position (instead of the expected guanosine). The relative abundances ofthese −1U 5′-tRFs exhibit ratios that are maintained constant across allexamined cancer types and in both health and disease. The analyses alsorevealed wide-ranging associations between tRFs on one hand, and mRNAsand proteins on the other. Many of the (positive and negative)associations involve partners that cross organelle boundaries: forexample, they involve tRFs that arise from nucleus-encoded tRNAs andmRNAs whose proteins localize in the MT; or, tRFs that arise fromMT-encoded tRNAs and mRNAs whose proteins localize in the nucleus. Theseassociations provide new insights to understanding the layer ofpost-transcriptional regulation. Moreover, in the short term, theserelationships suggest intriguing novel viewpoints from which to studyinter-organelle communication. In the longer term, there is greatpotential in leveraging these relationships to develop novel diagnosticsand novel therapeutics that are tuned to individual cancers.

We note that we carried out our study with full understanding that thepresence of documented modifications across the span of tRNAs (50-56)has the potential to pause or stop reverse transcription. Two recentlyreported methods (57,58) introduced a demethylation step that was shownto improve the enumeration of tRFs for some isoacceptors. It isconceivable that the 20,722 tRFs we have identified are but a subset ofa larger class of tRFs that are active in cancer tissues. By studyingthe TCGA datasets, we work with the best and most comprehensive datasetsthat are available for the time being. Even though these datasets arearguably incomplete, they remain invaluable in helping us shed a firstlight on important characteristics of tRFs across tissues.

A key finding of the analysis is the diversity of the identified tRFs.We mined a total of 20,722 tRFs that range widely in terms of abundance,length, structural type, and the location of their 5′ and 3′ termini.The tRFs also depend on the identity of the corresponding templateisodecoder/isoacceptor (10,59) and the identity of the genome (nuclearvs. mitochondrial) hosting the tRNAs from which the tRFs arise (22). Thetype of the cancer that is analyzed each time further modulates thesetRF attributes; we will return to this point below. Given that ourcomputational pipeline complements other available methods by exhibitingsuperior sensitivity and specificity (60), our results significantlyenrich the publicly available data with new information that can bereadily exploited in future studies.

Approximately one third of all identified tRFs are of ambiguous origin.In other words, if one examines the entirety of the human genome, thesequences of these tRFs can be found within annotated tRNAs as well asat loci that contain only partial instances (e.g., one half or onethird) of mature tRNA sequences (4,9,22). Some of these loci resemblefull-length tRNAs (4,61) whereas other loci correspond to partial tRNAs,repeat elements or mRNAs (4), and, possibly, non-transcribing sequences.Recognizing this complication, in parallel work, we designed andimplemented MINTmap (60), a freely-available tool that facilitates theidentification of tRFs of ambiguous genomic provenance. Strictlyspeaking, ambiguous tRFs require special attention, particularly whenexperimental work is being considered, as they cannot be linkedunequivocally to transcription from a tRNA template. We providedexamples of non-exclusive tRFs that are correlated with mRNAs containingan embedded instance of the corresponding tRF. Even though there werefew such examples in TCGA, they warrant caution because their biogenesismay not be linked to tRNA transcripts.

The 22 mitochondrial tRNAs were found to be very strong contributors tothe pool of distinct tRFs, when compared to the 610 nucleus-encodedtRNAs. In fact, 29% of all discovered tRFs derive from the 22 MT tRNAs(Supp. Table S1). This finding mirrors our previous results (4,8,9,22)and extends them to the numerous human tissues that are part of TCGA.Moreover, MT-tRNA-derived tRFs show marked differences when comparedwith the nuclear-tRNA-derived tRFs. Indeed, for a given cancer type, themitochondrial tRFs differ from their nuclear counterparts in length,relative abundances, dominant structural category, etc.

Even when we confine ourselves to a specific genome, i.e., nuclear orMT, we find a strong dependence of the tRF populations on the identityof the parental isoacceptor. These populations change across cancertypes and are characterized by differences in the structural type of theproduced tRFs (FIG. 1 ), the identity of the isoacceptor that producesmost distinct tRFs (FIG. 2 ), and the relative abundances of the tRFs(Supp. Table S3).

Moreover, the tRF populations show cancer-dependent differences withregard to the specific endpoints that are favored by tRFs of a givenstructural type (Supp. Table S3). Even if we ignore thiscancer-dependence and look at the structural types holistically, it isevident that the 3′ termini of 5′-tRFs and the 5′ termini of 3′-tRFsspan a large number of choices (FIG. 3 ). Notably, these preferences arevery similar to what was reported recently for the plant A. thaliana(24), which suggests common underlying biogenetic mechanisms and,possibly, functions. Furthermore, the cancer-dependence of the observedfragments suggests a tissue-specific dimension in the biogenesis oftRFs. This notion is supported, at least in part, by recent resultsshowing that the channeling of tRNAs into the miRNA Dicer-Ago pathwaydepends on the structure of the RNA molecule (62). Given that RNAfolding is a dynamic process (63), we posit that the observeddifferences in tRF cleavage patterns among tissues are caused bydifferences in each tissue's molecular physiology.

The i-tRFs, a novel structural type that we discovered recently (4),exhibit the largest diversity in TCGA. i-tRFs represent more than 75% ofthe 20,722 identified tRFs. As FIG. 3 shows, i-tRFs have a multitude ofpreferred starting and ending positions. The choice of these endpointsstrongly depends on cancer type (Supp. Table S3).

Despite the pronounced dependence of tRF profiles on cancer type, someisoacceptors stand out by producing tRFs with profiles that remainexceptionally consistent in healthy and diseased tissues, and across allcancer types. Of note here is the nuclear tRNA^(HisGTG) that produces−1U 5′-tRFs with lengths that range between 16 and 22 nt and haveabundances that are characterized by a unique property. Specifically,the abundances of −1U 5′-tRFs with 3′ termini that differ by a single nt(all these tRFs share the same 5′ terminus) alternate between high andlow, whereas their ratios remain constant across all analyzed normal andcancer samples, and all 32 cancer types. The resulting ‘see-saw’ patternspans a limited and persistent range of ratios that can be seen in FIG.4 and FIG. 14 . It should be stressed, however, that even though theratios of these −1U tRFs remains constant, their absolute abundances dochange from cancer type to cancer type. We did not find any otherisoacceptors whose tRFs exhibited this unusual behavior. The exquisitestability of these ratios across tissues, and the uniqueness oftRNA^(HisGTG) in this regard among tRNAs, leads us to conjecture thatthese −1U 5′-tRFs participate in fundamental cellular processes that arecurrently unknown.

Of equal importance is the finding that across all human tissues that weexamined, the 5′-tRFs from tRNA^(HisGTG) contain primarily a uracil atthe His(−1) position. This is a new and unexpected finding, because themature tRNA^(HisGTG) requires guanylation of its 5′-terminus before itcan be recognized by its cognate aminoacyl tRNA synthetase. Bycomparison, the levels of 5′-tRFs from tRNA^(HisGTG) with G, A, or C atthe −1 position were low. Recent work with the human breast cancer modelcell line BT-474 suggests that −1U 5′-tRFs from the tRNA^(HisGTG) locusarise from the mature tRNA (28). However, it is unclear for the timebeing whether the −1U 5′-tRFs that we discovered in the multitude ofhuman tissues that we analyzed above arise from the processing of themature tRNA^(HisGTG) or its precursor. Further complicating thisdetermination is the fact that the DNA template at four of the 12genomic loci encoding isodecoders of tRNA^(HisGTG) contains a T at the−1 position.

Given the nascent nature of this field and the apparent diversity andcontext-specific nature of the tRFs, it is not surprising that verylittle is known currently about their functional roles. With that inmind, we placed particular emphasis on leveraging the TCGA datasets toshed as much light as possible on this question. First, we found pairsof tRFs that are correlated across samples. Within a given cancer type,the same tRFs were found correlated across all available samples.However, different groups of tRFs were correlated across cancers (FIG.16B). We then extended this analysis to protein-coding transcripts andfound a very rich repertoire of negative correlations involving tRFs andspecific mRNAs. These tRF-mRNA anti-correlations depended strongly oncancer type (FIG. 17E). Earlier reports by others and us providedevidence of tRFs acting like miRNAs via Argonaute loading (4,10,29). Inlight of this, we also identified the group of mRNAs that are negativelycorrelated with miRNAs. The miRNA-mRNA anti-correlations dependedstrongly on cancer type as well (FIG. 17E).

We wish to stress one point here. It is entirely possible that directmolecular coupling drives some of the uncovered correlations. However,in the absence of any additional information, it will be prudent totreat these relationships as associations. For example, theseassociations could result from a common upstream regulator, frombelonging to the same pathway, or because some tRFs arise from the sameprecursor transcript. Considering the apparent diversity across cancertypes, it appears that it will be necessary to unravel the mechanismsunderlying the correlation patterns separately for each cancer.Moreover, the presented analysis makes it evident that tRFs havetissue-specific roles that are also more diverse than those of miRNAs(see below). Regarding the diversity in function, Ago-loaded tRFs arebut one of multiple facets of tRF biology. Indeed, one should alsorecognize the interaction of tRFs with other RNA binding proteins andwith the translation machinery (1,5,64).

Even though the specific mRNAs that are found associated with tRFsdiffer between cancer types, the pathways to which these mRNAs belongshow striking similarities across cancers. This observation is supportedthrough a DAVID analysis of gene ontology terms that reveal foursuper-groups: cell adhesion, chromatin organization, and developmentalprocesses (FIG. 18D). Additionally, our analysis generated severalobservations that were reported recently in the literature. For example,we found several correlations involving tRFs from isoacceptors of Gly,Asp, Glu, and Tyr with the mRNAs of HMGA1, CD151, CD97 and TIMP3: thesemRNAs were recently reported to be controlled by tRFs from these tRNAsin a YBX1-dependent manner (64). Additionally, we enumerated more than3,000 correlations of tRFs with ribosomal proteins, either mitochondrialor cytoplasmic, as well as more than 100 correlations of tRFs withaminoacyl tRNA synthetases, particularly IARS and MARS, which is inagreement with previous work in the field (5,65).

We examined at the isoacceptor level the correlations of theabove-mentioned four super-groups of mRNAs with tRFs. We split the mRNAsinto those that are negatively correlated with tRFs only and those thatare negatively correlated with both tRFs and miRNAs in the same cancertype. In each case, we computed the fraction of the 32 cancer typessupporting a specific “tRF isoacceptor—GO term” or a specific “miRNA+tRFisoacceptor—GO term” relationship. This revealed a tight coupling ofspecific isoacceptors with specific GO categories that persists acrossmultiple cancer types (FIG. 5A), but is manifested by different tRF-mRNApairings in each cancer (FIG. 17E). This suggests the existence of apreviously unrecognized tight coupling between MT and nuclear processes.

The seeming diversity of negatively-correlated tRFs and mRNAs in theface of persistent relationships between tRFs and pathways made usexamine the cellular localization of proteins whose mRNAs are negativelycorrelated exclusively with either miRNAs or tRFs. Specifically, in thecase of miRNAs we examined the individual isomiRs that are produced bythe various miRNA loci that are transcribed in the TCGA samples that weanalyzed. Juxtaposing the findings for isomiRs and tRFs, and doing soseparately for positive and negative correlations, revealed a strikingdichotomy (FIG. 5B). Specifically, we found that the localizationpatterns of proteins produced by mRNAs that are correlated with isomiRsare largely unchanged across the 32 cancer types. On the other hand,proteins produced by mRNAs that are correlated with tRFs showlocalization patterns that depend strongly on cancer type.

It is important to note here that, by comparison to miRNAs, themechanisms of biogenesis and function of tRFs remain poorly understoodfor the most part. Nonetheless, as we mentioned above, it is known thatshort tRFs are loaded on Argonaute and act like miRNAs. With that inmind, let us assume for the moment that the uncovered anti-correlationsimply tRF-mediated regulatory events that mirror the action of miRNAs onmRNAs. Then, our findings (FIG. 5A) suggest an intriguing “division oflabor” where some mRNAs are regulated solely by miRNAs, some solely bytRFs, and some by both miRNAs and tRFs. This synergistic hypothesis isfurther supported by the findings that are summarized by FIG. 5B andindicate that tRFs are likely involved in cell-type-dependentinteractions, analogously to what we reported previously for miRNAs(66). An instance of this dynamic and context-dependent network ofinteractions was shown for tRFs from tRNA^(Gln) that interact with YBX1in breast cancer cell lines (64) but not in cervical cancer cell lines(65).

Earlier (30-32) and more recent work (15) on the non-random placement ofrepeat elements on the genome as well as the finding that repeatelements become demethylated as stem cell differentiation progresses(67), led us to examine one more possibility. Specifically, we examinedpossible associations between tRFs and the sequence composition of thegenomic loci for mRNAs participating in these identified positive andnegative correlations. We analyzed unspliced mRNAs separately from theirintronic and exonic regions. We also distinguished between repeatelements that are sense with regard to the studied sequences and onesthat are antisense. We found a strong enrichment of sense repeats in themRNAs that are anti-correlated with tRFs with the bulk of the signalbeing contributed by the intronic regions of these mRNAs (FIG. 5C). Theobserved enrichments depend strongly on cancer type, and on the categoryof the embedded repeats. The fact that tRFs have different correlationswith repeat elements in different cancer types suggests a complexsystem-wide interaction network and a compendium of associated molecularevents that differ from cancer type to cancer type. Currently, the rolesof repeat elements in human cancers are not understood. However, theobserved enrichments are statistically significant (p-val≤0.001). Thus,it is reasonable to assume that these wide-ranging associations areactively leveraged by the cancer cell (68) in ways that remain to beelucidated.

We note that several of the GO terms that are part of the four generalpathways we described above (cell adhesion, chromatin organization, anddevelopmental processes) are significantly over-represented in the groupof genes that overlap with Alu elements (32). Our results on the link oftRFs with repeat elements come on the heels of two recent and relatedpublications. First, tRFs from the tRNA^(GlyGCC) isoacceptor were shownto repress expression of genes associated with the retroelement MERVL inmice (15). Second, tRFs were shown to increase in Arabidopsis pollen ina Dicer-dependent manner and to specifically target transposableelements (69). It is unclear currently whether the tRFs in human cancersact in a way similar to what is suggested in plants, i.e. to suppresstransposon activity. Notably, the fact that tRFs have differentcorrelations with repeat elements in different cancer types suggests acomplex system-wide interaction network and a compendium of associatedmolecular events that differ from cancer type to cancer type. Thesecorrelations and data could start shedding light on the peculiar rolesof repeat elements in human diseases and cancers (70).

Previously, we demonstrated for miRNA isoforms that their abundanceprofiles in human tissues depend on a person's sex, population origin,and race (71), as well as on tissue, tissue state and disease subtype(72). We also demonstrated that miRNAs are not unique in this regard andthat tRNA fragments have the exact same dependency on sex, populationorigin, race, tissue, tissue state and disease subtype (4,9). Workingwith the TCGA samples we had the opportunity to evaluate the possibilitythat similar dependencies might exist in LUAD, TNBC, and BLCA. In thecase of LUAD, we highlighted a dependency of tRF profiles on sex (FIG. 6). In the case of TNBC, we highlighted a dependency of tRF profiles onthe patient's race (FIG. 19 ). In the case of BLCA, the sex-dependentcorrelations that emerged are striking: we found that specificisoacceptors have a propensity for producing tRFs whose correlationswith mRNAs depend strongly on the patient's sex (FIG. 11A). In terms ofpathways, we found the greatest differences in genes regulating the cellcycle (FIG. 11B). These findings suggest the likely involvement of tRFsin the molecular events underlying sex-based disparities in BLCA.

Considering the emergence of tRFs as regulatory molecules in their ownright, such dependencies are expected to modulate the regulatory eventsunderlying a given disease in ways that have not been previouslyconsidered.

The multitude and diversity of the uncovered tRFs, and the multiplicityof associations between tRNA fragments and various cancers, suggest thata lot more work will be required before the community can improve itsunderstanding of the roles of tRFs in the cancer context. To facilitateinvestigations, we enhanced our MINTbase repository (22) with a modulethat is specific to TCGA. The module provides access to all of the tRFsthat we mined from TCGA. Importantly, the module permits very involvedinteractions with the contents of MINTbase by allowing elaborate searchrequests that require only minimal effort on the part of the user. Westress that although the TCGA portion of MINTbase is static, itsnon-TCGA portion is dynamic and growing steadily through thecontributions of tRF profiles by different research teams. We designedthe TCGA module in a way that permits users to compare TCGA findingswith the ever-growing non-TCGA data.

In summary, analysis of the entirety of the TCGA repository revealed avery rich population of tRNA fragments. The identities and relativeabundances of these fragments depend on cancer type. They also depend onthe identity of the parental isoacceptor. Yet, tRF profiles remainessentially constant within samples of the same cancer type,underscoring the constitutive nature of these fragments. These tRFsexhibit strong associations with one another and with other moleculartypes such as mRNAs (and, by extension, miRNAs) suggesting the existenceof numerous regulatory interactions that await discovery andcharacterization.

Methods

Datasets

11,198 short RNA datasets were downloaded on Oct. 16, 2015 from TCGA'sCancer Genomic Hub (CGHub). We used datasets from both normal and tumorsamples, which are identified by their TCGA barcode tag (01A, 01B and01C for tumor; 11A, 11B and 11C for normal). These datasets already hadadaptors trimmed and were converted back to FASTQ format using BamUtil'sbam2FastQ tool (http://genome.sph.umich.edu/wiki/BamUtil—version1.0.10). For each of these datasets, tRF profiles were generated usingMINTmap60 and default settings. These profiles have been incorporated inMINTbase22.

Our analyses focused exclusively on whitelisted datasets. Generally,non-whitelisted samples are marked for withdrawal by the various TCGAprojects for reasons that range from incorrect pathologic diagnosis toexclusion on the basis of patient medication history. Clinical metadatawere downloaded from TCGA's data portal on Oct. 28, 2015. To helpeliminate problematic and outlier samples that were identified by thevarious TCGA working groups, only datasets that did not have any specialannotation notes within the clinical metadata were included (n=10,274).

The Various Categories of tRFs

In terms of structural type, the tRFs overlapping a mature tRNA sequencefall in one of five possible categories (1,60): a) 5′-tRNA halves or‘5′-tRHs’ (5,6,73,74); b) 3′-tRNA halves or ‘3′-tRHs’ (2,75,76); c)5′-tRFs2,75,76; d) 3′-tRFs2,75,76; and, e) the “internal tRFs” or“i-tRFs” that we discovered and reported recently (4).

In terms of genomic origin, we characterize tRFs as “exclusive” or“ambiguous.” The sequences of exclusive tRFs are encountered only withinthe span of mature CCA-containing tRNAs, and appear nowhere else on thegenome. Ambiguous tRFs on the other hand have sequences that can befound both in mature tRNAs (the “tRNA space”) and elsewhere on thegenome. We recently published a methodology and standalone tool thatautomates the mining of tRFs from human RNA-seq datasets andautomatically tags them as exclusive or ambiguous (60). Our analyseswere based on both exclusive and ambiguous tRFs.

In terms of length, we generated tRFs with lengths between 16 and 30 ntinclusive. It is important to note here that the short RNA-seq profilesfor the samples of the TCGA repository were generated by runningdeep-sequencing for 30 cycles. Although adequate for miRNAs, 30 cycleswill generate inaccurate profiles for those tRFs that are longer than 30nt. In the various TCGA datasets, these longer tRFs appear truncatedand, thus, are represented in the TCGA as “30-mers.” Our parallel work(7) as well as our previous analyses of TCGA from BRCA subtypes (4), andof non-TCGA datasets from prostate cancer (8) and liver cancer (22) showthat there exist many distinct tRFs with length >30 nt that are veryabundant. Moreover, in the case of TCGA BRCA, we found that the “30-mer”tRFs are differentially abundant between normal breast and BRCA (4),suggesting an association with disease states. We note that the adaptercutting step may have shortened artificially long tRFs into “30-mers”, aproblem that does not arise when analyzing shorter molecules such asmiRNAs (77). Most of the analyses described below were based on tRFswith lengths 16-27 nt. Lest we miss potential important associations, weincluded tRFs with length 28-30 nt in those instances where doing so waswarranted.

Nomenclature and Notation

We adhere to NIH/TCGA's definition of race: White (Wh) refers to personwith origins in any of the original peoples of the far Europe, theMiddle East, or North Africa; and Black or African American (B/Aa)refers to persons with origins in any of the black racial groups ofAfrica. Based on the provided information, the majority of TCGA samplesare from either Wh or B/Aa donors. Smaller groups of samples wereobtained from donors who are: a) American Indian or Alaska Native (i.e.,persons having origins in any of the original peoples of North and SouthAmerica, including Central America, and who maintain tribal affiliationor community attachment), b) Asian (i.e. persons having origins in anyof the original peoples of the Far East, Southeast Asia, or the Indiansubcontinent including, e.g., Cambodia, China, India, Japan, Korea,Malaysia, Pakistan, the Philippine Islands, Thailand, and Vietnam); and,c) Native Hawaiian or other Pacific Islander (i.e., persons havingorigins in any of the original peoples of Hawaii, Guam, Samoa, or otherPacific Islands).

tRNA Cleavage Patterns

For each of the 32 cancer types, we examined the following attributes:

-   -   location within the mature tRNA of the tRFs' 5′ termini;    -   nucleotide composition within a rolling dinucleotide window that        surrounds the 5′ termini (positions −2/−1, −1/5′terminus,        5′-terminus/+1, +1/+2);    -   location within the mature tRNA of the tRFs' 3′ termini;    -   nucleotide composition within a rolling-dinucleotide window        surrounding the 3′ termini, as above; and,    -   location of the tRFs' 5′ and 3′ termini with respect to the        mature tRNA endpoints and upstream-stem or downstream-stem of        the nearest loop (D, anticodon, or T), as applicable.

Support for each of the attributes was calculated using tRFs abovethreshold. For each attribute, and for the cancer being studied, wecalculated its normalized support in two ways: a) by considering onlydistinct tRF sequences ignoring their abundance; and, b) by repeatingthe analysis taking into account the abundance of the tRFs.

FIG. 14 lists the complete set of histograms for all of the attributesthat we tracked and all tRF categories. In addition to showing theresults for each of the 32 cancers, we also provided histograms thatcombine the findings from all 32 cancer types.

NMF Analyses

The TCGA working groups have been making great use of non-negativematrix factorization (78), or NMF, to cluster in an unsupervised mannerthe microRNAs (miRNAs) in the generated RNA-seq datasets. For thisstudy, we replicated the NMF approach pioneered by the TCGA workinggroups leveraging tRF profiles (instead of miRNA profiles). We ran NMF(with R's NMF module, version 0.20.6) in an unsupervised manner usingthe top 30% most variable tRFs that passed Threshold-seq and had meanRPM>=1. Only tRFs with lengths 16-27 nt inclusive were used in theseanalyses. For each cancer type, the input used during clustering was amatrix comprising the RPM-normalized tRF profiles of the whitelisteddatasets (see above) for the cancer type. Only the tumor datasets ofeach cancer type were used. For SKCM, NMF clustering was carried outseparately on the primary tumor and the metastatic samples. Silhouettewidths were generated from the final NMF consensus membership matrix(n=500 iterations per run). NMF79 was run using values of k ranging from2 through 10 inclusive. For GBM, NMF clustering was not carried outbecause of the small number (5) of available datasets.

Correlation Analyses

For each cancer type separately, we first filtered the tRFs, miRNAs andthe genes based on their abundance. We used the isomiR profiles asconstructed for our previous study (66). For increased stringency in thecorrelation analyses among tRFs, miRNAs/isomiRs and mRNAs, we requiredthat the median normalized abundance of each miRNA or isomiR and tRF wasat least 2 RPM within the group of samples under consideration. For themRNA profiles, we used TCGA's files of normalized results(“rsem_genes.normalized_results”) and kept the most expressed genes.Specifically, we filtered out all genes whose average abundance was lessthan the median of the means of abundances of all mRNAs across theprimary tumor samples of the group under consideration. Then, wecomputed all pairwise tRF-tRF Spearman correlation coefficients, as wellas all tRF-mRNA and all miRNA-mRNA Spearman correlation coefficients forall expressed genes. We corrected the P-values to FDR scores, using thep.adjust function in the R base package with the method argument ‘FDR.’We only kept correlation coefficients that had an FDR≤0.01 and anabsolute value larger than 0.333. For the sex- and race-specificnetworks, we relaxed the FDR threshold to 0.05. Determination of whichmolecules enter the correlation analyses was done separately for eachcancer type, sex (LUAD and BLCA), or race (TNBC). In all instances, wekept at most 5,000 top (for positive correlations) or at most 5,000bottom (for negative correlations) correlations. Computations were doneusing python and the numpy (version 1.11.1) and scipy (version 0.18.1)packages.

Probabilities for the tRF-tRF networks were computed as the number ofnodes that satisfy the respective criteria divided by the total numberof nodes in each network.

Pathway analysis was run separately for the collection of genes thatwere negatively correlated with a) tRFs, or b) miRNAs. Specifically,DAVID (version 6.8) (80) was run with these two collections of genes andthe overlap with GOTERM_BPFAT, GOTERM_MFFAT, GOTERM_CCFAT, and, KEGGPATHWAY terms was calculated and filtered at an FDR threshold of 5%. Thegenes that were used in the correlation analysis in each cancer servedas the background gene list for the DAVID tool.

Protein Localization

Information on protein localization was downloaded from UniProt (81) andonly the manually reviewed human proteome (queried on Nov. 27, 2016) wasused. For each cancer type and correlation group (positive or negative),the distribution of the localization of gene products was computed as apercentage in each of the following seven cellular destinations:nucleus, cytoplasm, endoplasmic reticulum or Golgi, mitochondrion, cellmembrane, secreted, and “other” organelles (e.g. vesicles, endosomes,unknown localization, etc.).

Overlap with RepeatMasker Entries

To calculate the overlap with RepeatMasker (http://www.repeatmasker.org;hg19 version 4.0.5) elements, we delineated the genomic span of a geneby forming the union of the genomic extent all its unspliced variants.We computed the frequency of the genes that contain at least one sense(respectively, antisense) instance of each family of repeat elements. Inorder to evaluate whether this ‘observed’ overlap corresponded toenrichment or depletion of repeat elements, we ran Monte-Carlosimulations to create an ‘expected’ distribution of overlap withRepeatMasker elements. In more detail, in each of 10,000 iterations werandomly selected from the total pool of genes included in thecorrelation analysis the same number of genes as the number of uniquegenes that were correlated with tRFs. For each cancer type, positive andnegative correlations were analyzed separately (a total of 64simulations each with 10,000 iterations). After each iteration, wecalculated the overlap with RepeatMasker elements as described above andused it to create the ‘expected’ distribution. Based on thisdistribution, we calculated the z-score of the observed enrichment foreach repeat family.

Disambiguation of the Genomic Origin of tRFs

To investigate whether non-exclusive tRFs as well as nuclear tRFs areenriched or depleted in our correlation analyses, we performedMonte-Carlo simulations analogously to the way we calculated overlapwith repeat elements. Specifically, we performed 10,000 iterations andin each one we calculated the ratio of non-exclusive tRF and of nucleartRF based on a randomly chosen set of tRFs equal in size to the set oftRFs participating in the tRF-mRNA correlations. This was carried outseparately for each cancer type.

Multivariate Statistical Analysis and Data Visualization

Hierarchical Clustering and Principal Component Analysis, as well asnetwork visualizations were run and plotted in R, as we previouslydescribed (4,66,72).

Disambiguation of the Genomic Origin of tRFs

Our recent publications extensively discussed the possibility of tRFs ofambiguous origin. We also described the choices that are available forworking with such tRFs (9,60).

Looking at our analyses globally, we find that an average of 42±3% ofthe tRFs that entered our correlation analyses are not exclusive to tRNAspace. However, the 58:42 split is not mirrored by tRFs that participatein statistically significant correlation pairs with mRNAs. In fact, ineach cancer type, we find a ratio that differs from the “expected” 58:42(FIG. 20A). For example, the majority of tRFs that are correlated withmRNAs in LIHC and TGCT are exclusive to tRNA space (z-score of ≤−3.0,for both positive and negative correlations, with reference to arandomly chosen set of tRFs) but the respective set of tRFs in LAML andLUSC is enriched in non-exclusive instances (z-score≥3.0, for bothpositive and negative correlations). We also note that there is apositive correlation between the number of non-exclusive tRFs and thenumber of nuclear tRFs in our correlation analysis (FIG. 20A).

Based on the above results, we checked whether our correlation analysesresults could be driven by events where an instance of a non-exclusivetRF overlaps with one or more mRNA transcripts. In such cases, we wouldexpect to see strong correlations between the tRF and the mRNA, acrossmultiple cancer types. To test this hypothesis, we enumerated allgenomic instances of non-exclusive tRFs outside of tRNA space. Then, wechecked whether ambiguous tRFs in tRF-mRNA pairs had a sense instanceinside the genomic span of the respective mRNA. We found only 23 suchinstances in 12 cancer types (FIG. 20B). Compared to the total number oftRF-mRNA correlation pairs, these 23 examples are too few and do notsupport the hypothesis that the observed tRF-mRNA correlations are theresult of coupled biogenetic mechanisms. Nonetheless, it is important tonote that such examinations may be necessary when working withnon-exclusive tRFs.

Therefore, we have identified that correlations have revealed mRNA thatwould not be predicted to be useful in the first place is sometimescorrelated for direct or proxy impact for specific cancers. Therefore,upon determining such elements from a cancer tissue, we can directly orby proxy (indirectly) impact the mRNA by increasing or decreasingconcentrations. For example, treatment of any one of the 32 cancers canbe impacted by application of therapeutics to the genes or the Sequencesidentified herein.

Supplemental Table Captions

Supp. Table S1|Statistics of the tRFs identified in the 10,274 TCGAdatasets.

Supp. Table S2|Length distributions of tRFs listed by genomic origin(nucleus or mitochondrion), structural category, and length.

Supp. Table S3|Probabilities for tRF-tRF pairs.

Supp. Table S4|List of positive and negative correlation between tRF-tRFand tRF-mRNA pairs for the case of BRCA. A complete list for all 32cancer types can be generated according to the methods described herein.

Supp. Table S5|DAVID analysis results for the genes whose mRNAs areanti-correlated with tRFs or miRNAs. GO terms and KEGG pathways areanalyzed and the results reported separately for each of 32 cancers.

Supp. Table S6|GO terms and descriptions of the four general clusters ofFIG. 5 .

Lengthy table referenced here US11715549-20230801-T00001 Please refer tothe end of the specification for access instructions.

Lengthy table referenced here US11715549-20230801-T00002 Please refer tothe end of the specification for access instructions.

Lengthy table referenced here US11715549-20230801-T00003 Please refer tothe end of the specification for access instructions.

Lengthy table referenced here US11715549-20230801-T00004 Please refer tothe end of the specification for access instructions.

Lengthy table referenced here US11715549-20230801-T00005 Please refer tothe end of the specification for access instructions.

Lengthy table referenced here US11715549-20230801-T00006 Please refer tothe end of the specification for access instructions.

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LENGTHY TABLES The patent contains a lengthy table section. A copy ofthe table is available in electronic form from the USPTO web site(https://seqdata.uspto.gov/?pageRequest=docDetail&DocID=US11715549B2).An electronic copy of the table will also be available from the USPTOupon request and payment of the fee set forth in 37 CFR 1.19(b)(3).

What is claimed is:
 1. A method of treating cancer in a patientcomprising administering a gene modulating therapy, wherein at least onegene that is transcribed in an ailing tissue of the patient iscorrelated with an abundance of at least one cancer regulating tRF thatis transcribed in the ailing tissue, wherein the ailing tissue and thetRF transcribed in the ailing tissue is selected from the groupcomprising: ACC (Adrenocortical carcinoma) and the tRF is selected fromthe group of sequences consisting of: SEQ ID NO: 1 through SEQ ID NO:31; BLCA (Bladder Urothelial Carcinoma) and the tRF is selected from thegroup of sequences consisting of: SEQ ID NO: 32 through SEQ ID NO: 40;BRCA (Breast invasive carcinoma) and the tRF is selected from the groupof sequences consisting of: SEQ ID NO: 41 through SEQ ID NO: 58; CESC(Cervical squamous cell carcinoma and endocervical adenocarcinoma) andthe tRF is selected from the group of sequences consisting of: SEQ IDNO: 59 through SEQ ID NO: 71; COAD (Colon adenocarcinoma) and the tRF isselected from the group of sequences consisting of: SEQ ID NO: 72through SEQ ID NO: 77; DLBC (Lymphoid Neoplasm Diffuse Large B-cellLymphoma) and the tRF is selected from the group of sequences consistingof: SEQ ID NO: 78 through SEQ ID NO: 88; ESCA (Esophageal carcinoma) andthe tRF is selected from the group of sequences consisting of: SEQ IDNO: 89 through SEQ ID NO: 94; HNSC (Head and Neck squamous cellcarcinoma) and the tRF is selected from the group of sequencesconsisting of: SEQ ID NO: 95 through SEQ ID NO: 109; KICH (KidneyChromophobe) and the tRF is selected from the group of sequencesconsisting of: SEQ ID NO: 110 through SEQ ID NO: 120; KIRC (Kidney renalclear cell carcinoma) and the tRF is selected from the group ofsequences consisting of: SEQ ID NO: 121 through SEQ ID NO: 128; KIRP(Kidney renal papillary cell carcinoma) and the tRF is selected from thegroup of sequences consisting of: SEQ ID NO: 129 through SEQ ID NO: 132;LAML (Acute Myeloid Leukemia) and the tRF is selected from the group ofsequences consisting of: SEQ ID NO: 133 through SEQ ID NO: 165; LGG(Brain Lower Grade Glioma) and the tRF is selected from the group ofsequences consisting of: SEQ ID NO: 166 through SEQ ID NO: 180; LIHC(Liver hepatocellular carcinoma) and the tRF is selected from the groupof sequences consisting of: SEQ ID NO: 181 through SEQ ID NO: 186; LUAD(Lung adenocarcinoma) and the tRF is selected from the group ofsequences consisting of: SEQ ID NO: 187 through SEQ ID NO: 194; LUSC(Lung squamous cell carcinoma) and the tRF is selected from the group ofsequences consisting of: SEQ ID NO: 195 through SEQ ID NO: 217; MESO(Mesothelioma) and the tRF is selected from the group of sequencesconsisting of: SEQ ID NO: 218 through SEQ ID NO: 235; OV (Ovarian serouscystadenocarcinoma), and the tRF is sequence SEQ ID NO: 236; PAAD(Pancreatic adenocarcinoma) and the tRF is selected from the group ofsequences consisting of: SEQ ID NO: 237 through SEQ ID NO: 242; PCPG(Pheochromocytoma and Paraganglioma) and the tRF is selected from thegroup of sequences consisting of: SEQ ID NO: 243 through SEQ ID NO: 251;PRAD (Prostate adenocarcinoma) and the tRF is selected from the group ofsequences consisting of: SEQ ID NO: 252 through SEQ ID NO: 261; READ(Rectum adenocarcinoma) and the tRF is selected from the group ofsequences consisting of: SEQ ID NO: 262 through SEQ ID NO: 270; SARC(Sarcoma) and the tRF is selected from the group of sequences consistingof: SEQ ID NO: 271 through SEQ ID NO: 275; SKCM (Skin CutaneousMelanoma) and the tRF is selected from the group of sequences consistingof: SEQ ID NO: 276 through SEQ ID NO: 283; STAD (Stomach adenocarcinoma)and the tRF is selected from the group of sequences consisting of: SEQID NO: 284 through SEQ ID NO: 291; TGCT (Testicular Germ Cell Tumors)and the tRF is selected from the group of sequences consisting of: SEQID NO: 292 through SEQ ID NO: 308; THCA (Thyroid carcinoma) and the tRFis selected from the group of sequences consisting of: SEQ ID NO: 309through SEQ ID NO: 324; THYM (Thymoma) and the tRF is selected from thegroup of sequences consisting of: SEQ ID NO: 325 through SEQ ID NO: 331;UCEC (Uterine Corpus Endometrial Carcinoma) and the tRF is selected fromthe group of sequences consisting of: SEQ ID NO: 332 through SEQ ID NO:344; and UVM (Uveal Melanoma) and the tRF is selected from the group ofsequences consisting of: SEQ ID NO: 345 through SEQ ID NO:
 372. 2. Themethod of claim 1 further comprising first collecting an ailing tissuesample from said patient and determining the correlation between the atleast one gene and the at least one tRF.
 3. The method of claim 1,wherein the ailing tissue and the tRF transcribed in the ailing tissueis selected from the group consisting of: PAAD (Pancreaticadenocarcinoma) and the tRF is selected from the group of sequencesconsisting of: SEQ ID NO: 237 through SEQ ID NO: 242; and COAD (Colonadenocarcinoma) and the tRF is selected from the group of sequencesconsisting of: SEQ ID NO: 72 through SEQ ID NO:
 77. 4. The method ofclaim 1, wherein the tRF and the gene are positively correlated.
 5. Themethod of claim 1, wherein the tRF and the gene are negativelycorrelated.
 6. The method of claim 1, further comprising the step ofdetermining the abundance of the tRF in the ailing tissue.
 7. The methodof claim 1, further comprising the step of determining the abundance ofthe gene in the ailing tissue.
 8. A method of treating cancer in apatient comprising administering a gene modulating therapy, wherein atleast one gene that is transcribed in an ailing tissue of the patient iscorrelated with an abundance of at least one cancer regulating tRF thatis transcribed in the ailing tissue, wherein the ailing tissue isselected from the group comprising: ACC (Adrenocortical carcinoma), andthe gene is selected from the group consisting of: CSDC2, CSGALNACTI,RERG, PCMTD1, PLCB3, YEATS2, BIRC2, MVP, MYST3, ARL6IP5, TRANK1,TMEM45A, ACVR1, PGCP, VCL, MSRA, C10orf54, DCUN1D3, CTDSPL2, SIK2,TMCO6, SRCAP, TMEM159, PLEKHO2, HLA-E, TAX1BP3, Cllorf75, RCE1, NDRG4,MRI, MARK2, FAM21B, HLA-B, RBL2, CABC1; BLCA (Bladder UrothelialCarcinoma), and the gene is selected from the group consisting of:ACTG2, TGFBR3, PRELP, RERE, OSR1, TCEAL1, NNAT, GCOM1, MMP2, MYST4,SYNPO2, C16orf45, FYCO1, MYH11, CSRP1, MEIS2, ACTA2, CLU, LOXL1, IGFBP4,TXNIP, SLIT3, CHRDL2, MYL9; BRCA (Breast invasive carcinoma), and thegene is selected from the group consisting of: CRTC1, CALCOCO1, SLC27A1,CROCC, PGPEP1, PSD4, TBC1D17, PHF15, ARAP1, TNFRSF14, NISCH, MED16,RGS12, MYO15B, AGXT2L2, RFX1, C21orf2, NEURL4, TPCN1, HOOK2, LTBP3,SPHK2, ABTB1, ABCD4, ZBTB48, CIRBP, CYTH2, ZNF446, PHF1, RPS9, MZF1,FAM160A2, KIF13B, GLTSCR2, WDR81, SH2B1, RHOBTB2, CRY2, LTBP4, HDAC7,ZNF219, MUM1, REMS, RAPGEF3, CCDC9; CESC (Cervical squamous cellcarcinoma and endocervical adenocarcinoma), and the gene is selectedfrom the group consisting of: NBPF10, JRK, SMGS, ALDH1A2, MFAP4, ZFYVE1,CDC42BPB, ENTPD4, IGF1, ZFYVE26, APOLD1, LOC200030, KIAA0430, UBN1,VASH1, RANBP10, WDR37, MGP, MON1E, CNN1, MAT2A, PGR, KIAA0100, Cl4orf21,HCFC1, LRP1O, DIDO1, FBXL18, ATP6V0A1, RGS2, MLXIP, TRIM56, CTGF,KIAA0284, DES, SFRP4, PDPK1, TAOK2, SMCR8, CLN8, UNC119B, TRIM25,CYB561D1, TBC1D2B, DNAJCS, CRAMP1L, ZNF646, ZC3HAV1, KHNYN, PSKH1, RGS1;COAD (Colon adenocarcinoma), and the gene is selected from the groupconsisting of: ATP8B2, SYNE1, WIPF1, AOC3, LIMS1, FZD1, CYBB, MAFB,GIMAP6, REST, STAB1, FPR3, MSRB3, FRMD6, CALCRL, MPEG1, MYLK, ELTD1,FGL2, SPARCL1, PLXDC2, LAIR1, ITGB2, NRP1, MRC1, ZEB1, SYT11, NCKAP1L,AXL, APLNR, ZEB2, EDIL3, FERMT2, PTPRM, RASSF2, PKD2, PHLDB2, TCF4,IL10RA, HEG1, HIPK3, NEXN, TMEM140, AMOTL1, A2M, TIE1, AKT3, CD163,LPHN2, OSMR, CSF1R, DAAM2, IL1R1, GPC6, SLC8A1, FBN1, GNB4, GPNMB,DOCK2, KIAA1462, CSF2RB, MYOSA, S1PR1, ARHGEF6; DLBC (Lymphoid NeoplasmDiffuse Large B-cell Lymphoma), GOLGA2, DCHS1, CLDND1, CSRNP2, FRMD8,SLCO2A1, ARHGAP23, NID1, DUSP7, TBC1D20, YAP1, WDR82, TMEM43, TJP1,CARDS, ZNF213, KIAA0232, EPAS1, VPS11, PHC1, SKI, DAG1, ANKRD40, FAT1,PHF12; ESCA (Esophageal carcinoma), and the gene is selected from thegroup consisting of: SSCSD, PDLIM3, CELF2, TIMP3, ABCC9, CALD1, COL8A1,GREM1, THBS4, PRUNE2, TMEM47, PBXIP1, PLN, CCDC80, C7, PODN, DDR2,PPP1R12B, MRVI1, LMOD1, C7orf58, HSPB7, TAGLN, PPP1R16B, GFRA1,LOC728264, SGCD, PGMS; HNSC (Head and Neck squamous cell carcinoma), andthe gene is selected from the group consisting of: GABBR1, C14orf179,METT11D1, C6orf125, ZNF692, FKBP2, FAM1 13A, LOC388789, TAZ, WASH3P,CDK5RAP3, PLBD2, SDR39U1, CPTIB, UBLS, C14orf2, LOC146880, THAP3,ANKRD13D, C12orf47, ATPSE, ATPIF1, SYF2, C8orf59, WASH7P, NPIPL3, CDK10,C1orf151, MRPS21, C19orf60, C7orf47, CENPT, GASS, KIFC2, NFIC, RPL39,UQCRB, COX6C, LUC7L, CCS, COMMD6, ZNF133, SNHG12, C11orf31, NPEPL1; KICH(Kidney Chromophobe), and the gene is selected from the group consistingof: BMPR1A, EXT1, TFAM, PDCD1 1, MTIF2, POLR3A, MAPK8, PRDX3, COQS; KIRC(Kidney renal clear cell carcinoma), and the gene is selected from thegroup consisting of: ARHGAP19, KIAA1671, KIAA0754, KIAA1 147, ZNF45,KLF13, MYO9A, FUT1 1, ASHIL, KIF13A, TUBGCP3, MTF1, FAM168A; KIRP(Kidney renal papillary cell carcinoma), and the gene is selected fromthe group consisting of: GLCCII, CDK13, POGZ, UBN2, CREBZF, NPHP3,VEZF1, CHD1, YPEL2, LRRC37B2, GPATCH8, ENC1, TTC18, Cl lorf61, RSBN1L,EFNB2, PHIP, RBAK, SPEN, RBM9, SMURF2, ZNF264, ZNF587, PTPN12, TPBG,RBM33, DMTF1, CCNT2, ARID4B, ARGLU1, CREB1, KIAA0753, BTAF1, C17orf85,RLF, MLLS, ZFC3H1, ZNF160, PRPF38B, SETDS, ARRDC4, HOOK3, RC3H1, MLL3,RNF207, MAP3K1, PLEKHH2, CCDC57, DAPK1, LUC7L3; LAML (Acute MyeloidLeukemia), and the gene is selected from the group consisting of:SUPTSH, SKIV2L, IKBKG, HGS, MIB2, MED15, STK25, ANAPC2, RHOT2, SFil,CUL9, ARHGEF1, GTPBP2, KIAA0892, MBD1, UCKL1, DHX16, ZFYVE27, APBA3,PI4KB, C19orf6, SPSB3, CAPN10, FLYWCH1, ATG4B, CDC37, LZTR1, MAN2C1,C1orf63, DVL1, EDC4, DHX34, PCNXL3, EXOC3, FUK, FBXL6, LMF2, HDAC1O,E4F1, TSC2, ZDHHC8, CPSF3L, FAM160B2, CLCN7, LRRC14, D2HGDH, ZNF335,FHOD1, SOLH, ZBTB17, POLRMT, SLC26A1, KIAA0415, SELO, SAPS2, NME3,KLHL36, SCYL1, USP19, DGKZ, CYHR1, ATG2A, VPS16, XAB2, ACTRS, ZNF76,ATP13A1, RNF31, GPN2, MUS81, FAM73B, TTC15, CXXC1, TRMT2A, WDR8, PTGES2,TEL02, RFNG, SLC39A13; LGG (Brain Lower Grade Glioma), and the gene is:EXD3; LIHC (Liver hepatocellular carcinoma), and the gene is DYRK2; LUAD(Lung adenocarcinoma), and the gene is selected from the groupconsisting of: CROCCLI, RHPNI, ABCA7, RGL3, PDXDC2, ENGASE, ATG16L2,CSAD, TTLL3, ARHGEF12, ANKS3, LOC100132287, SGSM2, HEXDC, LPIN3, ACCS,PLEKHMIP, ANO9, ELMOD3, KIAA0895L, APIG2, ACAP3, ECHDC2, NXFI,JMJD7-PLA2G4B, TMEM175, CCDC64B, ANKMYI; LUSC (Lung squamous cellcarcinoma), and the gene is selected from the group consisting of: PKDI,CHKB-CPTIB, WDR90, MACFI, RBM6, LENG8, TAFIC, COL16A1, CAPN12, RBM39,ACINI, FNBP4, PILRB, DMPK, SFRSS, AHSA2, RBM25, PLCGI, SNRNP70,NCRNA00201, GIGYFI, SRRM2, GOLGA8B, ZGPAT, RTELI, COL27A1, MAPK8IP3,PABPCIL, HSPG2, AKAP13, LRPI, NKTR, ATAD3B, TUBGCP6, ZNF276, MICALL2,CLCN6, NSUN5P2, NEATI, LAMAS, CHD2, PPPIR12C, FAM193B, NPIP, CDKIIA,STX16, LTBP2, LOC91316, NBEAL2, FLJ45340, LRDD, CCDC88B, GOLGA8A; MESO(Mesothelioma), and the gene is selected from the group consisting of:C15orf40, SNUPN, CHTF8, CLNSIA, CSNKID, DPF2, PCIFI, DNAJC4, SECISBP2,C5orf32, RPRDIB, RPL38, NDUFA10, RPRM, BAT4, RSLIDI; OV (Ovarian serouscystadenocarcinoma), and the gene is selected from the group consistingof: KIAA0907, ULK3; PAAD (Pancreatic adenocarcinoma), and the gene isselected from the group consisting of: NUAKI, KBTBD4, HMCNI, DSTYK; PCPG(Pheochromocytoma and Paraganglioma), and the gene is selected from thegroup consisting of: CACNA2D1, TP53BP1, KIAA1244, MARCH8, PCDHGC4,ESYT2, DHX15, TECPRI, MAP3K2, TBCID24, PCDHI, IPOI 1, MGATS, TRAM2,ADAMI0, GNA1 1, CBX6, SNURF, RIF1, CNTN1, LMBRD2, CANDI, TRIP12, RC3H2,PAK3, TMCO3, CSNK2AIP, ASBI, AKAP2, ROCK2, NUP155, PIK3C3, KLHDC10,RAB35, GTF2I, HSPA8, FAM49A; PRAD (Prostate adenocarcinoma), and thegene is selected from the group consisting of: FEM1B, TGOLN2, SEPT9,MYOCD, LUZP1, TLNI, PIASI, RNFI 11, DCBLD2, URB1, ZBTB40, ZNF516,ATXNIL, RHBDD1, HUWEI, VP513D, ITPRI, NNT, ERCI; READ (Rectumadenocarcinoma), and the gene is selected from the group consisting of:FZD4, CD93, DYNCII2, ENG, ELK3, KDR, FAM1OIB, PXDN, GIMAP4, F13A1,VCAMI, ARHGAP31, CD34, GNG2, LCP2, VWF, CSF1, GPR116, KIRREL, MMRN2,ETSI, ITGA4, FAM120B; SARC (Sarcoma), SH3BGRL, SORBSI, MAPK4, LYNXI,MICAL3, AKAP1, LIMS2, RNF38, LOC283174, CAND2, PLIN4, MOAP1, RNF19A,RABGAP1, C5orf4, FRY, ARHGEF1 7, SETMAR, SSH3, NUMA1, PBXI, TORIAIP1,TACC2, RAB3D, BBSI, CEP68, GPRASPI, SVIL, CRBN, CRTC3, ZFYVE21, SLMAP,RASL12, SCAPER, STAT5B, ZAK, EZHI; SKCM (Skin Cutaneous Melanoma), andthe gene is selected from the group consisting of: MLL4, LMTK2, APBB3,C17orf56, LOC388796, ANO8; STAD (Stomach adenocarcinoma), and the geneis selected from the group consisting of: KCNMAI, MAST4, FHL1, ATP2B4,TENC1, C20orf194, ASB2, C10orf26, TTC28, FAM13B, ITPKB, GNAO1, FAM129A,ZBTB4, FNBPI, FLNA, CCDC69, STONI, NFASC, PAPLN, ADCY5, LPP, NEGRI,ABBBP, INPP5B, TNXB, ANGPTLI, ANK2, EPHA3, ZCCHC24, SETBPI, PRICKLE2,LTBP1, RGMA, DARC, KANK2, SYNPO; TGCT (Testicular Germ Cell Tumors), andthe gene is selected from the group consisting of: ATF6, CTDSP2, MKL2,TEAD1, ZNF407, TRAPPC9, AHDC1, LANCL1, KCTD20, OXR1, SNX1, CSNK1G1,KIAA0247, LDOCIL, EPCI, GRLFI, ABHD2, RAil, ARIDIB, ITFGI, MUT, KIAA1737, LAMA2, KIAA1 109, CCNI, DIXDCI, C6orf89, RNF144A, APPBP2, KLF12,ZFP91; THCA (Thyroid carcinoma), and the gene is selected from the groupconsisting of: KIAA0495, PHF21A, ZBTB5, SFRS6, NCOA5, ZNF814, IP6K2,IFT140, INTS3, ZNF559, SETD4, TGIF2, VILL, KCNC3, UBE2G2, FBXO9, IPW,DUOXA1, CACNA2D2, EFHC1, FAM189A2, GTF2IRD2P1, KIAA1683, AP4B1, SCAND2,CDRT4, UNKL, NYNRIN, ARMC5, MAPKBP1, USP40, VEGFA, OLFM2, FBF1, TCF7L1,MXD4, IKBKB, POFUT2, BOC, TCF7L2, RMST, TRO; THYM (Thymoma), and thegene is selected from the group consisting of: ZFYVE9, LOC399959, SIX1,PDGFC; UCEC (Uterine Corpus Endometrial Carcinoma), and the gene isselected from the group consisting of: RNMT, SON, MDN1, CELF1, RIPK1,YLPM1, XRN2, BPTF, RQCD1, PAFAH1B2, BOD1L, SBNO1, RNF169, PIK3R4, LRCH3,DCP1A, SF3A1, SLC9A8, TNRC6A, BRPF3; UCS (Uterine Carcinosarcoma), andthe gene is RHBDF1; and UVM (Uveal Melanoma), and the gene is selectedfrom the group consisting of: MAP1A, TCIRG1, ECM1, C14orf159, WARS,PCYOX1L.
 9. The method of claim 8, wherein the ailing tissue and thegene is selected from the group consisting of: PAAD (Pancreaticadenocarcinoma), and the gene is selected from the group consisting of:NUAKI, KBTBD4, HMCNI, DSTYK; and COAD (Colon adenocarcinoma), and thegene is selected from the group consisting of: ATP8B2, SYNE1, WIPF1,AOC3, LIMS1, FZD1, CYBB, MAFB, GIMAP6, REST, STAB1, FPR3, MSRB3, FRMD6,CALCRL, MPEG1, MYLK, ELTD1, FGL2, SPARCL1, PLXDC2, LAIR1, ITGB2, NRP1,MRC1, ZEB1, SYT11, NCKAP1L, AXL, APLNR, ZEB2, EDIL3, FERMT2, PTPRM,RASSF2, PKD2, PHLDB2, TCF4, IL10RA, HEG1, HIPK3, NEXN, TMEM140, AMOTL1,A2M, TIE1, AKT3, CD163, LPHN2, OSMR, CSF1R, DAAM2, IL1R1, GPC6, SLC8A1,FBN1, GNB4, GPNMB, DOCK2, KIAA1462, CSF2RB, MYOSA, S1PR1, ARHGEF6. 10.The method of claim 8 further comprising first collecting an ailingtissue sample from said patient and determining the correlation betweenthe at least one gene and the at least one tRF.
 11. A method of treatingcancer in a patient comprising: administering a gene modulating therapy,wherein at least one tumorigenic mRNA transcribed from the gene of anailing tissue of the patient is correlated with an abundance of at leastone cancer regulating tRF that is transcribed in the ailing tissue,wherein the method further comprises: (i) first collecting an ailingtissue sample from said patient and determining the correlation betweenthe at least one mRNA and the at least one tRF; (ii) computing anaverage negative correlations (A) for each tRF that exceed a threshold(T) in the ailing tissue, (iii) determining whether the average negativecorrelations (A) for each tRF is equal to or less than a secondthreshold (t) in the ailing tissue, wherein the at least one tRF isselected from the tRFs that were determined to have an average negativecorrelation (A) equal to or less than the second threshold (t), andwherein the ailing tissue and the tRF transcribed in the ailing tissueis selected from the group comprising: ACC (Adrenocortical carcinoma)and the tRF is selected from the group of sequences consisting of: SEQID NO: 1 through SEQ ID NO: 31; BLCA (Bladder Urothelial Carcinoma) andthe tRF is selected from the group of sequences consisting of: SEQ IDNO: 32 through SEQ ID NO: 40; BRCA (Breast invasive carcinoma) and thetRF is selected from the group of sequences consisting of: SEQ ID NO: 41through SEQ ID NO: 58; CESC (Cervical squamous cell carcinoma andendocervical adenocarcinoma) and the tRF is selected from the group ofsequences consisting of: SEQ ID NO: 59 through SEQ ID NO: 71; COAD(Colon adenocarcinoma) and the tRF is selected from the group ofsequences consisting of: SEQ ID NO: 72 through SEQ ID NO: 77; DLBC(Lymphoid Neoplasm Diffuse Large B-cell Lymphoma) and the tRF isselected from the group of sequences consisting of: SEQ ID NO: 78through SEQ ID NO: 88; ESCA (Esophageal carcinoma) and the tRF isselected from the group of sequences consisting of: SEQ ID NO: 89through SEQ ID NO: 94; HNSC (Head and Neck squamous cell carcinoma) andthe tRF is selected from the group of sequences consisting of: SEQ IDNO: 95 through SEQ ID NO: 109; KICH (Kidney Chromophobe) and the tRF isselected from the group of sequences consisting of: SEQ ID NO: 110through SEQ ID NO: 120; KIRC (Kidney renal clear cell carcinoma) and thetRF is selected from the group of sequences consisting of: SEQ ID NO:121 through SEQ ID NO: 128; KIRP (Kidney renal papillary cell carcinoma)and the tRF is selected from the group of sequences consisting of: SEQID NO: 129 through SEQ ID NO: 132; LAML (Acute Myeloid Leukemia) and thetRF is selected from the group of sequences consisting of: SEQ ID NO:133 through SEQ ID NO: 165; LGG (Brain Lower Grade Glioma) and the tRFis selected from the group of sequences consisting of: SEQ ID NO: 166through SEQ ID NO: 180; LIHC (Liver hepatocellular carcinoma) and thetRF is selected from the group of sequences consisting of: SEQ ID NO:181 through SEQ ID NO: 186; LUAD (Lung adenocarcinoma) and the tRF isselected from the group of sequences consisting of: SEQ ID NO: 187through SEQ ID NO: 194; LUSC (Lung squamous cell carcinoma) and the tRFis selected from the group of sequences consisting of: SEQ ID NO: 195through SEQ ID NO: 217; MESO (Mesothelioma) and the tRF is selected fromthe group of sequences consisting of: SEQ ID NO: 218 through SEQ ID NO:235; OV (Ovarian serous cystadenocarcinoma), and the tRF is sequence SEQID NO: 236; PAAD (Pancreatic adenocarcinoma) and the tRF is selectedfrom the group of sequences consisting of: SEQ ID NO: 237 through SEQ IDNO: 242; PCPG (Pheochromocytoma and Paraganglioma) and the tRF isselected from the group of sequences consisting of: SEQ ID NO: 243through SEQ ID NO: 251; PRAD (Prostate adenocarcinoma) and the tRF isselected from the group of sequences consisting of: SEQ ID NO: 252through SEQ ID NO: 261; READ (Rectum adenocarcinoma) and the tRF isselected from the group of sequences consisting of: SEQ ID NO: 262through SEQ ID NO: 270; SARC (Sarcoma) and the tRF is selected from thegroup of sequences consisting of: SEQ ID NO: 271 through SEQ ID NO: 275;SKCM (Skin Cutaneous Melanoma) and the tRF is selected from the group ofsequences consisting of: SEQ ID NO: 276 through SEQ ID NO: 283; STAD(Stomach adenocarcinoma) and the tRF is selected from the group ofsequences consisting of: SEQ ID NO: 284 through SEQ ID NO: 291; TGCT(Testicular Germ Cell Tumors) and the tRF is selected from the group ofsequences consisting of: SEQ ID NO: 292 through SEQ ID NO: 308; THCA(Thyroid carcinoma) and the tRF is selected from the group of sequencesconsisting of: SEQ ID NO: 309 through SEQ ID NO: 324; THYM (Thymoma) andthe tRF is selected from the group of sequences consisting of: SEQ IDNO: 325 through SEQ ID NO: 331; UCEC (Uterine Corpus EndometrialCarcinoma) and the tRF is selected from the group of sequencesconsisting of: SEQ ID NO: 332 through SEQ ID NO: 344; and UVM (UvealMelanoma) and the tRF is selected from the group of sequences consistingof: SEQ ID NO: 345 through SEQ ID NO:
 372. 12. The method of claim 11,wherein the mRNA and the tRF are positively correlated.
 13. The methodof claim 11, wherein the mRNA and the tRF are negatively correlated. 14.The method of claim 11 further comprising the step of determining theabundance of the mRNA in the ailing tissue.
 15. The method of claim 11further comprising the step of determining the abundance of the tRF inthe ailing tissue.
 16. The method of claim 11, wherein the ailing tissueand the tRF transcribed in the ailing tissue is selected from the groupconsisting of: PAAD (Pancreatic adenocarcinoma) and the tRF is selectedfrom the group of sequences consisting of: SEQ ID NO: 237 through SEQ IDNO: 242; and COAD (Colon adenocarcinoma) and the tRF is selected fromthe group of sequences consisting of: SEQ ID NO: 72 through SEQ ID NO:77.
 17. A method of treating cancer in a patient comprisingadministering a gene modulating therapy, wherein at least onetumorigenic mRNA transcribed from the gene of an ailing tissue of thepatient is correlated with an abundance of at least one cancerregulating tRF that is transcribed in the ailing tissue, furthercomprising first collecting an ailing tissue sample from said patientand determining the correlation between the at least one mRNA and the atleast one tRF, wherein the ailing tissue is selected from the groupcomprising: ACC (Adrenocortical carcinoma), and the at least one mRNA istranscribed from a gene selected from the group consisting of: CSDC2,CSGALNACTI, RERG, PCMTD1, PLCB3, YEATS2, BIRC2, MVP, MYST3, ARL6IP5,TRANK1, TMEM45A, ACVR1, PGCP, VCL, MSRA, C10orf54, DCUN1D3, CTDSPL2,SIK2, TMCO6, SRCAP, TMEM159, PLEKHO2, HLA-E, TAX1BP3, C11orf75, RCE1,NDRG4, MRI, MARK2, FAM21B, HLA-B, RBL2, CABC1; BLCA (Bladder UrothelialCarcinoma), and the gene is selected from the group consisting of:ACTG2, TGFBR3, PRELP, RERE, OSR1, TCEAL1, NNAT, GCOM1, MMP2, MYST4,SYNPO2, C16orf45, FYCO1, MYH1 1, CSRP1, MEIS2, ACTA2, CLU, LOXL1,IGFBP4, TXNIP, SLIT3, CHRDL2, MYL9; BRCA (Breast invasive carcinoma),and the gene is selected from the group consisting of: CRTC1, CALCOCO1,SLC27A1, CROCC, PGPEP1, PSD4, TBC1D17, PHF15, ARAP1, TNFRSF14, NISCH,MED16, RGS12, MYO15B, AGXT2L2, RFX1, C21orf2, NEURL4, TPCN1, HOOK2,LTBP3, SPHK2, ABTB1, ABCD4, ZBTB48, CIRBP, CYTH2, ZNF446, PHF1, RPS9,MZF1, FAM160A2, KIF13B, GLTSCR2, WDR81, SH2B1, RHOBTB2, CRY2, LTBP4,HDAC7, ZNF219, MUM1, REMS, RAPGEF3, CCDC9; CESC (Cervical squamous cellcarcinoma and endocervical adenocarcinoma), and the gene is selectedfrom the group consisting of: NBPF10, JRK, SMGS, ALDH1A2, MFAP4, ZFYVE1,CDC42BPB, ENTPD4, IGF1, ZFYVE26, APOLD1, LOC200030, KIAA0430, UBN1,VASH1, RANBP10, WDR37, MGP, MONIE, CNN1, MAT2A, PGR, KIAA0100, C14orf21,HCFC1, LRP1O, DIDO1, FBXL18, ATP6V0A1, RGS2, MLXIP, TRIM56, CTGF,KIAA0284, DES, SFRP4, PDPK1, TAOK2, SMCR8, CLN8, UNC119B, TRIM25,CYB561D1, TBC1D2B, DNAJCS, CRAMP1L, ZNF646, ZC3HAV1, KHNYN, PSKH1, RGS1;COAD (Colon adenocarcinoma), and the gene is selected from the groupconsisting of: ATP8B2, SYNE1, WIPF1, AOC3, LIMS1, FZD1, CYBB, MAFB,GIMAP6, REST, STAB1, FPR3, MSRB3, FRMD6, CALCRL, MPEG1, MYLK, ELTD1,FGL2, SPARCL1, PLXDC2, LAIR1, ITGB2, NRP1, MRC1, ZEB1, SYT11, NCKAP1L,AXL, APLNR, ZEB2, EDIL3, FERMT2, PTPRM, RASSF2, PKD2, PHLDB2, TCF4,IL10RA, HEG1, HIPK3, NEXN, TMEM140, AMOTL1, A2M, TIE1, AKT3, CD163,LPHN2, OSMR, CSF1R, DAAM2, IL1R1, GPC6, SLC8A1, FBN1, GNB4, GPNMB,DOCK2, KIAA1462, CSF2RB, MYOSA, S1PR1, ARHGEF6; DLBC (Lymphoid NeoplasmDiffuse Large B-cell Lymphoma), GOLGA2, DCHS1, CLDND1, CSRNP2, FRMD8,SLCO2A1, ARHGAP23, NID1, DUSP7, TBC1D20, YAP1, WDR82, TMEM43, TJP1,CARDS, ZNF213, KIAA0232, EPAS1, VPS11, PHC1, SKI, DAG1, ANKRD40, FAT1,PHF12; ESCA (Esophageal carcinoma), and the gene is selected from thegroup consisting of: SSCSD, PDLIM3, CELF2, TIMP3, ABCC9, CALD1, COL8A1,GREM1, THBS4, PRUNE2, TMEM47, PBXIP1, PLN, CCDC80, C7, PODN, DDR2,PPP1R12B, MRVI1, LMOD1, C7orf58, HSPB7, TAGLN, PPP1R16B, GFRA1,LOC728264, SGCD, PGMS; HNSC (Head and Neck squamous cell carcinoma), andthe gene is selected from the group consisting of: GABBR1, C14orf179,METT11D1, C6orf125, ZNF692, FKBP2, FAM1 13A, LOC388789, TAZ, WASH3P,CDK5RAP3, PLBD2, SDR39U1, CPTIB, UBLS, C14orf2, LOC146880, THAP3,ANKRD13D, C12orf47, ATPSE, ATPIF1, SYF2, C8orf59, WASH7P, NPIPL3, CDK10,C1orf151, MRPS21, C19orf60, C7orf47, CENPT, GASS, KIFC2, NFIC, RPL39,UQCRB, COX6C, LUC7L, CCS, COMMD6, ZNF133, SNHG12, C11orf31, NPEPL1; KICH(Kidney Chromophobe), and the gene is selected from the group consistingof: BMPR1A, EXT1, TFAM, PDCD1 1, MTIF2, POLR3A, MAPK8, PRDX3, COQS; KIRC(Kidney renal clear cell carcinoma), and the gene is selected from thegroup consisting of: ARHGAP19, KIAA1671, KIAA0754, KIAA1 147, ZNF45,KLF13, MYO9A, FUT1 1, ASHIL, KIF13A, TUBGCP3, MTF1, FAM168A; KIRP(Kidney renal papillary cell carcinoma), and the gene is selected fromthe group consisting of: GLCCII, CDK13, POGZ, UBN2, CREBZF, NPHP3,VEZF1, CHD1, YPEL2, LRRC37B2, GPATCH8, ENC1, TTC18, C1 1orf61, RSBN1L,EFNB2, PHIP, RBAK, SPEN, RBM9, SMURF2, ZNF264, ZNF587, PTPN12, TPBG,RBM33, DMTF1, CCNT2, ARID4B, ARGLU1, CREB1, KIAA0753, BTAF1, C17orf85,RLF, MLLS, ZFC3H1, ZNF160, PRPF38B, SETDS, ARRDC4, HOOK3, RC3H1, MLL3,RNF207, MAP3K1, PLEKHH2, CCDC57, DAPK1, LUC7L3; LAML (Acute MyeloidLeukemia), and the gene is selected from the group consisting of:SUPTSH, SKIV2L, IKBKG, HGS, MIB2, MED15, STK25, ANAPC2, RHOT2, SFil,CUL9, ARHGEF1, GTPBP2, KIAA0892, MBD1, UCKL1, DHX16, ZFYVE27, APBA3,PI4KB, C19orf6, SPSB3, CAPN10, FLYWCH1, ATG4B, CDC37, LZTR1, MAN2C1,C1orf63, DVL1, EDC4, DHX34, PCNXL3, EXOC3, FUK, FBXL6, LMF2, HDAC1O,E4F1, TSC2, ZDHHC8, CPSF3L, FAM160B2, CLCN7, LRRC14, D2HGDH, ZNF335,FHOD1, SOLH, ZBTB17, POLRMT, SLC26A1, KIAA0415, SELO, SAPS2, NME3,KLHL36, SCYL1, USP19, DGKZ, CYHR1, ATG2A, VPS16, XAB2, ACTRS, ZNF76,ATP13A1, RNF31, GPN2, MUS81, FAM73B, TTC15, CXXC1, TRMT2A, WDR8, PTGES2,TEL02, RFNG, SLC39A13; LGG (Brain Lower Grade Glioma), and the gene is:EXD3; LIHC (Liver hepatocellular carcinoma), and the gene is: DYRK2;LUAD (Lung adenocarcinoma), and the gene is selected from the groupconsisting of: CROCCLI, RHPNI, ABCA7, RGL3, PDXDC2, ENGASE, ATG16L2,CSAD, TTLL3, ARHGEF12, ANKS3, LOC100132287, SGSM2, HEXDC, LPIN3, ACCS,PLEKHMIP, ANO9, ELMOD3, KIAA0895L, APIG2, ACAP3, ECHDC2, NXFI,JMJD7-PLA2G4B, TMEM175, CCDC64B, ANKMYI; LUSC (Lung squamous cellcarcinoma), and the gene is selected from the group consisting of: PKDI,CHKB-CPTIB, WDR90, MACFI, RBM6, LENG8, TAFIC, COL16A1, CAPN12, RBM39,ACINI, FNBP4, PILRB, DMPK, SFRSS, AHSA2, RBM25, PLCGI, SNRNP70,NCRNA00201, GIGYFI, SRRM2, GOLGA8B, ZGPAT, RTELI, COL27A1, MAPK8IP3,PABPCIL, HSPG2, AKAP13, LRPI, NKTR, ATAD3B, TUBGCP6, ZNF276, MICALL2,CLCN6, NSUN5P2, NEATI, LAMAS, CHD2, PPPIR12C, FAM193B, NPIP, CDKIIA,STX16, LTBP2, LOC91316, NBEAL2, FLJ45340, LRDD, CCDC88B, GOLGA8A; MESO(Mesothelioma), and the gene is selected from the group consisting of:C15orf40, SNUPN, CHTF8, CLNSIA, CSNKID, DPF2, PCIFI, DNAJC4, SECISBP2,C5orf32, RPRDIB, RPL38, NDUFAIO, RPRM, BAT4, RSLIDI; OV (Ovarian serouscystadenocarcinoma), and the gene is selected from the group consistingof: KIAA0907, ULK3; PAAD (Pancreatic adenocarcinoma), and the gene isselected from the group consisting of: NUAKI, KBTBD4, HMCNI, DSTYK; PCPG(Pheochromocytoma and Paraganglioma), and the gene is selected from thegroup consisting of: CACNA2D1, TP53BP1, KIAA1244, MARCH8, PCDHGC4,ESYT2, DHX15, TECPRI, MAP3K2, TBCID24, PCDHI, IPOI 1, MGATS, TRAM2,ADAMI0, GNA1 1, CBX6, SNURF, RIF1, CNTN1, LMBRD2, CANDI, TRIP12, RC3H2,PAK3, TMCO3, CSNK2AIP, ASBI, AKAP2, ROCK2, NUP155, PIK3C3, KLHDC10,RAB35, GTF2I, HSPA8, FAM49A; PRAD (Prostate adenocarcinoma), and thegene is selected from the group consisting of: FEM1B, TGOLN2, SEPT9,MYOCD, LUZP1, TLNI, PIASI, RNFI 11, DCBLD2, URB1, ZBTB40, ZNF516,ATXNIL, RHBDD1, HUWEI, VPS13D, ITPRI, NNT, ERCI; READ (Rectumadenocarcinoma), and the gene is selected from the group consisting of:FZD4, CD93, DYNCII2, ENG, ELK3, KDR, FAM1OIB, PXDN, GIMAP4, F13A1,VCAMI, ARHGAP31, CD34, GNG2, LCP2, VWF, CSF1, GPR116, KIRREL, MMRN2,ETSI, ITGA4, FAM120B; SARC (Sarcoma), SH3BGRL, SORBSI, MAPK4, LYNXI,MICAL3, AKAP1, LIMS2, RNF38, LOC283174, CAND2, PLIN4, MOAP1, RNF19A,RABGAP1, C5orf4, FRY, ARHGEF1 7, SETMAR, SSH3, NUMA1, PBXI, TORIAIP1,TACC2, RAB3D, BBSI, CEP68, GPRASPI, SVIL, CRBN, CRTC3, ZFYVE21, SLMAP,RASL12, SCAPER, STAT5B, ZAK, EZHI; SKCM (Skin Cutaneous Melanoma), andthe gene is selected from the group consisting of: MLL4, LMTK2, APBB3,C17orf56, LOC388796, ANO8; STAD (Stomach adenocarcinoma), and the geneis selected from the group consisting of: KCNMAI, MAST4, FHL1, ATP2B4,TENC1, C20orf194, ASB2, C10orf26, TTC28, FAM13B, ITPKB, GNAO1, FAM129A,ZBTB4, FNBPI, FLNA, CCDC69, STONI, NFASC, PAPLN, ADCY5, LPP, NEGRI,ABBBP, INPP5B, TNXB, ANGPTLI, ANK2, EPHA3, ZCCHC24, SETBPI, PRICKLE2,LTBP1, RGMA, DARC, KANK2, SYNPO; TGCT (Testicular Germ Cell Tumors), andthe gene is selected from the group consisting of: ATF6, CTDSP2, MKL2,TEAD1, ZNF407, TRAPPC9, AHDC1, LANCL1, KCTD20, OXR1, SNX1, CSNK1G1,KIAA0247, LDOCIL, EPCI, GRLFI, ABHD2, RAil, ARIDIB, ITFGI, MUT,KIAA1737, LAMA2, KIAA1 109, CCNI, DIXDCI, C6orf89, RNF144A, APPBP2,KLF12, ZFP91; THCA (Thyroid carcinoma), and the gene is selected fromthe group consisting of: KIAA0495, PHF21A, ZBTB5, SFRS6, NCOA5, ZNF814,IP6K2, IFT140, INTS3, ZNF559, SETD4, TGIF2, VILL, KCNC3, UBE2G2, FBXO9,IPW, DUOXA1, CACNA2D2, EFHC1, FAM189A2, GTF2IRD2P1, KIAA1683, AP4B1,SCAND2, CDRT4, UNKL, NYNRIN, ARMC5, MAPKBP1, USP40, VEGFA, OLFM2, FBF1,TCF7L1, MXD4, IKBKB, POFUT2, BOC, TCF7L2, RMST, TRO; THYM (Thymoma), andthe gene is selected from the group consisting of: ZFYVE9, LOC399959,SIX1, PDGFC; UCEC (Uterine Corpus Endometrial Carcinoma), and the geneis selected from the group consisting of: RNMT, SON, MDN1, CELF1, RIPK1,YLPM1, XRN2, BPTF, RQCD1, PAFAH1B2, BOD1L, SBNO1, RNF169, PIK3R4, LRCH3,DCP1A, SF3A1, SLC9A8, TNRC6A, BRPF3; UCS (Uterine Carcinosarcoma), andthe gene is: RHBDF1; and UVM (Uveal Melanoma), and the gene is selectedfrom the group consisting of: MAP1A, TCIRG1, ECM1, C14orf159, WARS,PCYOX1L.
 18. The method of claim 17, wherein the ailing tissue is COAD(Colon adenocarcinoma), and the gene is selected from the groupconsisting of: ATP8B2, SYNE1, WIPF1, AOC3, LIMS1, FZD1, CYBB, MAFB,GIMAP6, REST, STAB1, FPR3, MSRB3, FRMD6, CALCRL, MPEG1, MYLK, ELTD1,FGL2, SPARCL1, PLXDC2, LAIR1, ITGB2, NRP1, MRC1, ZEB1, SYT11, NCKAP1L,AXL, APLNR, ZEB2, EDIL3, FERMT2, PTPRM, RASSF2, PKD2, PHLDB2, TCF4,IL10RA, HEG1, HIPK3, NEXN, TMEM140, AMOTL1, A2M, TIE1, AKT3, CD163,LPHN2, OSMR, CSF1R, DAAM2, IL1R1, GPC6, SLC8A1, FBN1, GNB4, GPNMB,DOCK2, KIAA1462, CSF2RB, MYOSA, S1PR1, ARHGEF6.
 19. The method of claim17, wherein the ailing tissue is PAAD (Pancreatic adenocarcinoma), andthe gene is selected from the group consisting of: NUAKI, KBTBD4, HMCNI,DSTYK.